{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 新一轮空值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 忽略警告信息\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('datas/los_data.csv')\n",
    "train = data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看空值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNullCount():\n",
    "    x = train.isnull().sum()\n",
    "    print(x[x>0])\n",
    "    x[x>0].sort_values().plot.bar()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage      259\n",
      "Alley           1369\n",
      "MasVnrType         8\n",
      "MasVnrArea         8\n",
      "BsmtQual          37\n",
      "BsmtCond          37\n",
      "BsmtExposure      38\n",
      "BsmtFinType1      37\n",
      "BsmtFinType2      38\n",
      "Electrical         1\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e106828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## 1. LotFrontage 填充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 思路1：房子宽度，是否和小区有关？和小区设计有关？\n",
    "\n",
    "取不同的Neighborhood的均值和中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c907da0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c474438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot('LotFrontage', 'Neighborhood', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getNullCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhood_group=train.groupby('Neighborhood')\n",
    "lot_median=neighborhood_group['LotFrontage'].median()\n",
    "lot_mean=neighborhood_group['LotFrontage'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对空值情况\n",
    "# train[train['LotFrontage'].isnull()]['Neighborhood']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 思路2：是否和LotArea 有关呢？\n",
    "\n",
    "房子宽度 和 房屋面积(不缺失)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42609501877180833"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['LotFrontage'].corr(train['LotArea'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6020022167939361"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['LotFrontage'].corr(np.sqrt(train['LotArea']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 添加1列\n",
    "train['SqrtLotArea']=np.sqrt(train['LotArea'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>SqrtLotArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "      <td>91.923882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "      <td>97.979590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "      <td>106.066017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "      <td>97.724101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "      <td>119.415242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities     ...      PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub     ...         NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub     ...         NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub     ...         NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub     ...         NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub     ...         NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold SaleType  SaleCondition  SalePrice  SqrtLotArea  \n",
       "0   2008       WD         Normal     208500    91.923882  \n",
       "1   2007       WD         Normal     181500    97.979590  \n",
       "2   2008       WD         Normal     223500   106.066017  \n",
       "3   2006       WD        Abnorml     140000    97.724101  \n",
       "4   2008       WD         Normal     250000   119.415242  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11c860ba8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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I2g6dg1HiyeItqtMTjvHJB1t5tmMAgOVN7mkVy5uK97SK2QrMoDZFLnvAlwJvAf4gIs+mrn0MuEFEzgcMsB+4CcAY84KI/AB4EXcFxc26AkKpqZVCOclf7j7GZ36+k8HUpOB155/BX25YMaOgKgU+j0VdhY/qYOYHf0opbEs8lfXr15sdO3YUuhlKFUSxl5OMJmzu3rqX/3vOfcNbE/Ty4atX80dnNhW4ZdlliVBX4aM25EsfSsloTKV8VzgrVcaKvZzk3u4h7tjSyoHeCADrltRxyzVraKoKFLhl2SMiVAe91Ff4Z12fQgNYqRJTzOUkjTH87zOH2bytnYRt8FjCuy5dxhsvbCm50yqmUhnw0lDpn/N6ZQ1gpUpIMZeT7IvE+deHdvK7fb0ALKoLceumNaxZUFPglmVPwOehsdJPMEvj1xrASpWIYi4n+eT+Xu56sI2+iLv1+apz5vPey8+iwl8eEePzWNRX+rNel6I8/nWUKnPFWk4ynnSL6Pz3U4cAqPR7+MCrV3H5mnkFbll2eCyhLuSnJuTNyVplDWClitxAJMHx4eIL34O9Ee7Y0sqe7iEAzl5Yw22b1rKgtvRPqxARaoJe6uYwwZYJDWCliljvcJz+SHGVkzTG8MAfOvnS43uIpk6r+IuXL+Gtlywri9MqqgJe6rMwwZYJDWClilQxlpMMRxN85uFdbNt1DIDmqgAf27SG8xbXFbhlcxf0eWjI4gRbJjSAlSoyxVpO8rlD/XzigTa6U2PRG1Y28cErV81o51cx8nksGir9VBag8LsGsFJFpBjLSdqO4Vu/PcC3f3cAx0DQa3HzK8/i2nMXlHQRHY8l1FX4qQnmZoItExrAShUJxzF0haOMxIunBErnQJQ7H2jlhSODAJzVXMVtm9aypLF0T6sQEWpDPupCPqwCj1lrACtVBIqxnORjbd187uFdDKf+IPzZyxbzrsuW4/eW7mkVVUEvDRV+vEVy4oYGsFIFVmzlJEfiNl94bDc/e8Et3V1f4eMjV6/houUNBW7Z7IX8Huor8jvBlgkNYKUKqNjKSe7sDHPnA60c6hsB4KLlDXz4qtU0VPoL3LLZ8XksGqv8RbsjrzhbpdRpoJjKSTrG8IMdh/ivX+0j6Rh8HuE9f7yCP123qCSL6BTDBFsmMgrg1OkWfwGsMMbcLiJLgAXGGD2xQqlZiCXdimbFUE7y+FCMux5s46mD/QAsaajgtk1rOWteVYFbNnPFNMGWiUx7wF8GHOBy4HYgjHvW24U5apdSZauYykn+Zu8xPv2zXQykNnz8yUsX8lcbzyRUZGOlmSi2CbZMZBrALzfGrBORZwCMMX0iUpqDQkoVULGUk4wlbO7Z1s6Pnz0CQHXQywevXMWGlaV30njI7+5gC3hL749GpgGcEBEP7jluiEgzbo9YKZWhYiknue/YMHdsaWXfsWEAzltcy0evWcO8mtIqouP3ujvYinWCLROZtvwLwI+AeSJyJ/AG4LactUqpMlMM5SSNMdz/+yPc/Yt24qkiOm//o2XccNGSkiqi47Us6ip91JT4FmjIMICNMd8RkaeAV+EeNvdaY0xrTlumVJkohnKSA5EEn/75Tn6z9zgAC2uD3HrtWs4+o3ROq7BSE2y1JTLBlolMV0E0AN3A99Ku+YwxxVWqSakiUwzlJJ8+0McnH2zj+LDbjletmcf7r1iZ9dMdcqk66KO+wldSE2yZyPS/wNNAC9CH2wOuAzpFpAt4jzHmqRy1T6mSVehykknb4b9+vZ/vP9mBAUI+D++/YiVXnj2/YG2aqQq/l/pKX0lOsGUi0wB+GPihMeZnACJyJfB64Ou4S9RenpvmKVWausPRgpaTPNw3wh0PtLKzMwzAmgXV3LppLYvqQgVr00z4vRaNlQFC/vIM3lGZBvDFxpj3jH5jjPm5iPybMeYmEQnkqG1KlZxCl5M0xvDwi118/tE9jCRsBLjhohbe/kfLSuLtu9eyqK/0lXyN4UxlGsBHReQjwH2p7/8c6EotTdPlaErhhl/nYOHKSQ7Fkvz7I7t5rK0bgMYqPx+9Zg3rltQXpD0zYYlQV+FOsBXz1uFsyzSA3wT8I/Dj1Pe/Tl3zAG/MQbuUKilOqpxktEDlJF84MsCdW9roHIwCcOmZjfz9VaupDRV/T7I66KOhMreHXxarTJehHQPee4qb92SvOUqVHtsxHB0YKUg5SdsxfPeJg3zzN/txjDt2+tcbz+T/vXRh0fckK/xeGir9JV1feK4yXYbWDHwYOAcY2y5jjLk8R+1SqiQkbYejBSon2T0Y5RMPtvHcoQEAVjRVcuumtSxvqsx7W2bidJlgy0Smf3q+A7QBy4F/BvYDT071ABFpEZHHReRFEXlBRN6fut4gIg+LyO7U5/rUdRGRL4jIHhF5TkTWzfqnUioPEgUM3227enj3vU+Nhe+fXrCIL//FuqIOX69l0VwdYHF9hYZvSqZjwI3GmK+JyPuNMb8AfiEiUwYwkAQ+aIx5WkSqgadE5GHg7cCjxpi7ROQW4BbgI8A1wMrUx8uBu9HlbapIxZI2XQOxvNfyHUnYfPnxvWz5w1EAakM+PnzVai45szGv7ZiJ03WCLRMZF+NJfT4qIpuAI8CU55MYY44CR1Nfh0WkFVgEXAdsTN3tm8BW3AC+DrjXuJVKtotInYgsTD2PUkUjmrDpGsx/Ld/dXWHu2NJKR+q0ipctreeWq1fTWFWcK0FFhOqgl/qK03OCLROZBvAdIlILfBD4IlAD/G2mLyIiy4ALgN8B89NCtRMY3ZazCOhIe9ih1LVxASwiNwI3AixZsiTTJiiVFSNxN3zzWcvXMYb/efowX/1lOwnb4LWEd122nD9bv7hoT6uoDLjBezpPsGUi0wDuM8YMAAPAKwFE5NJMHigiVbjF2//WGDOY/hbEGGNEZEa/ycaYzcBmgPXr1xe+orU6bRSilm/vcJx/faiNJ/b3AbC4PsRtm9ayan513towEwGfh8bK4jv8slhlGsBfBCZOik12bRwR8eGG73eMMf+butw1OrQgIgtxi/wAHMatNzFqceqaUgVXiFq+T+zr5VMPtdEXcUcAr3nJAv7mlWcV5QSWz2NRX+kvqQI/xWDKfy0RuQT4I6BZRP4u7aYa3E0YUz1WgK8BrcaYz6bddD/wNuCu1OefpF3/GxG5D3fybUDHf1UxyHct33jS4T9/2c7/PO32PyoDHj746lVsXD0vb23IlMcS6kJ+akLFffhlsZruz5UfqErdL/09zyBuUfapXAq8BfiDiDybuvYx3OD9gYi8CzjAiZ10DwDX4m7siADvyPBnUCpn+iNxeofzV07ywHH3tIq9Pe5pFS85o4aPbVrLgiI7rUJEqAl6qdMJtjmRTN5SichSY8yBPLRnRtavX2927NhR6GaoMpXPWr7GGB74QydfenwP0dRpFW+5eClvvnhp0QVcVcBLfaUfXwkU9ymgjP6jZTpgExCRzcCy9MfoTjhVrvJZy3dwJMFnH97Ftt3HAJhXHeDWa9dy7uLavLx+poI+9/BLnWDLnkwD+L+BrwBfBQpTbUSpPDDG0DMUy1st39939POJB9roGXLHmDeuaubvXr2KqmDxTGb5PO7hl5U6wZZ1mf6LJo0xd+e0JUoVmDGGrsEYkXjuwzdpO9y7/QDf2X4QAwR9Fu+9fCVXnzO/aCazdIIt9zIN4P8Tkb/GPRl5bDrYGNObk1YplWeOY+gK56eW79GBEe7c0sqLR93TKlbOq+K2TWtpaajI+WtnYnSCrb7CXzaHXxarTAP4banPH0q7ZoAV2W2OUvlnp2r5xvJQy/fR1m7+/ZFdDKeC/o3rF/Ouy5YXzYSWTrDlV6b1gJfnuiFq9ra2dXPPtnY6+iK01Fdw04YVbFxTfGtGi1G+yklG4km+8Ogefv5iFwANlX5uuXo165dNWVIlb3SCrTAyrQfsA/4K2JC6tBW4R4+lL7ytbd18/P4X8HmEupCP7nCUj9//AreDhvA0ErZDZx7Ct61zkDu2tHKk3z2t4uIVDXzoqtXUV/hz+rqZ0Am2wsr0X/1uwId7AjK4GyzuBt6di0apzN2zrR2fR6jwu/8pK/xeIvEk92xr1wCeQj7KSdqO4ftPdvD13+zHdgw+j3DThjN53QVnFHxSy2MJdRV+aoI6wVZImQbwhcaY89K+f0xEfp+LBqmZ6eiLUDfh3K+Qz8OhvkiBWlT88lFOsicc45MPtvFsRz8ASxsruG3TWs5srsrZa2ZCRKgN+agL+XSCrQhkGsC2iJxpjNkLICIr0PXARaGlvoLucHSsBwxu0e7F9cUxo15s8lFO8td7jvHpn+1kMLWW+DXnncFfvWIFgQKPr1YFvTRU+EviePrTRaYB/CHgcRFpx91itxSt1VAUbtqwgo/f/wKReJKQz8NIwiZhG27aoAtUJhqOJenOYUWzWMLm7l+0c//vjwBQE/TyoatWc+lZTTl5vUyF/O4EW8CrE2zFZtoAFhELGME9Kmh16vJOY0z+ykOpU9q4Zh63444F7+4OE086+DzCPdvax26fq3JYZRGOJujJYUWz9p4h7tjSyv7j7tDP+S11fPSaNTRXF+60Cp/HorHKP+7dkSoumRbjecYYc0Ee2jMjWoznhPTVEOk94dtfc86cwjJXz5tPA5EEx4dzE77GGH787BG+8ou9JGyDJfDOS5fz5xe2FKyIjteyqKv0URP0TX9nlSsZ/cfPdDDoURF5veh0adFKXw0h4n5O7wkX2/PmS99wPGfh2x+Jc+uPn+eLj+0hYRsW1gb54g0X8KaXLylI+IoI9RV+FteHNHxLRKbvTW4C/g5IikgUN92NMaYmZy1TM5Kr1RClvMoilxXNnjrQx10PtnE8VSv4yrPn897LzyrYetrqoI/6Cp9OsJWYTHfCFecBVGpMNldDpI/5Do4kSNoOzdUnCoKXwiqL7nA0JxXNErbD1361jx/sOARAhd/DB65YyavWzp/mkbmhE2ylbco/lyLyN2lfn5P75qjZumnDChK2IRJPYoz7eTarIUbHfLvDUepCPioDHnqG4vSEo3N63nxxK5rlJnw7eiO893vPjIXv2oXVbH7LywoSvn6vxcLaEAtrQxq+JWy6HvA7gf9Iff0tpjmEUxVO+mqIQ30RFs9ytcLEnXVNVW7PdzhmMzCSmPXz5kOuKpoZY3johS6++NhuogkHAd708iW87ZKleX/L77Us6it9VOsYb1mYyYCVTsAVuY1r5s05GCcb822sDOC1EvzyI8V7AEquKpoNRZN89uFdbN3VA0BTlZ+PXbuW81vqsvo607FEqKvwURvy6dbhMjJdANeJyOtwhypqRORP029MO2pelYlS3FlnO4ajAyPEk9mt6/D84QHufKCVrkF3FcUfr2zig69eRU0of71PEaE6VZu32M6GU3M3XQD/AnhN6uttwP9Lu80AGsBlptR21uWiopntGL69/QDf2n4Ax0DAa3HzK89i07kL8tr7rAx4adDavGVtygA2xrwDQESWG2P2pd8mIlojuAjNdddatsaS8yGedMM3mxXNOgejfPKBVv5weBCAM5sruW3TWpY2VmbtNaajtXlPH5nuhHvaGLNuwrWnjDEvy1nLMqA74cYrh11rmcpFRbOtO3v4zMM7GY6548ivX7eI9/zxCvze/PRAtTZvWZn7sfQisgY4B6idMP5bAwQnf5QqlNOlNnA0YdM5kL2KZiNxm/94fA8PPt8JQF3Ix4evXs3FKxqz8vzT0dq8p6/p/tSuBv4EqGP8+G8YeE+uGqVmp5R3rWUqEk/SNZi9ima7usLcsaWVQ30jAFy4rJ6PXL2Ghsrcn1Yh4p5iUqu1eU9b040B/wT4iYhcYoz5bZ7apGapFFcwzEQ4muDYUDwr4esYww+fOsRXf7mPpGPwWsJ7/ng5r3/ZYqw89EJ167CCzIvxdIjIj0SkO/XxPyKyOKctUzOWrd1wxWhgxC0nmY3w7R2Oc8v//IGv/KKdpGNoqQ/xpTddwJ+tb8l5+Fb4vSyur6C5OqDhqzLeiPF14LvAn6W+f3Pq2qtz0ahSke86udO9XimtYJiJ/kic3lTRm7na3n6cTz20k4FUkZ5rz13Aza88i1COVxwEfB4aKvyE/LqyQZ2Q6SqI3084Ew7N3qfaAAAgAElEQVQRedYYc37OWpaBQq6CyPeKg9NphUO640OxsbCci3jSYfO2dv73mcMAVAW8fPDKVbxiVfOcn3sqPo9FfaWfKl3ZcLrJaj3gYyLyZhHxpD7eDByf8tVF/is1XPF82rV/EpHDIvJs6uPatNs+KiJ7RGSniFyVYbsKJt91cku9Lu9sdIejWQnf/ceH+evvPj0WvucuquU/3/qynIavxxIaKwMsrg9p+JaQrW3d3LB5O5d96jFu2LydrW3dOX29TH8z3gl8Efgc7g643wBvn+Yx38At5HPvhOufM8b8W/oFETkbuB53ydsZwCMissoYU7QHf+Z7xcHo640erRO3HXyWZCWgio0xhu5wjOHY3CqaGWP46XNH+fLWvcSSDpbA2y5ZltOC6SJCTdBLnW4dLjnp7zLrQj66w1E+fv8L3E52jvaaTEY9YGPMAWPMa4wxzcaYecaY1wKvn+Yx24DeDNtxHXCfMSaW2nG3B7gow8cWREt9BSMTCr/kcsVBS30Fx4djHOmPknQMHktIOIZwNJnzv9L55KSK6sw1fAdGEvzj/S/yuUd2E0s6LKgJ8vnrz+ctlyzNWTBWBbwsrg/RWBXQ8C1BhXiXOZdp2L+b5eP+RkSeSw1R1KeuLQI60u5zKHXtJCJyo4jsEJEdPT09s2zC3OV7xcFNG1bQO5zAYNzjSFK7b+srfGUzDGE7hqODcy8n+czBPt5z7w5+tecYAK9c3czmt76Mc86ozUYzTxLyezijLsS8mqDWbShhHX2RkyZjc72Ofi6DU7P5E3838C+4wxj/AnwGd3gjY8aYzcBmcCfhZtGGrMj3ioONa+ZRFfAQTTjEbQe/x6KpKkh10DvnX5BiOPU4aTscnWNRnaTt8I3f7Od7T3RggKDP4n2Xr+Sqc+bnZIeZ3+tuHdZTh8tDIdbRz+U3Z8bhZ4zpGv1aRP4T+Gnq28NAS9pdF6euFbVs1N+diVXza076BYnEk3P6BSnEuNdE2ahodrh/hDu3tNLWGQZg9fxqbtu0lkX1oWw1c4yeOlyeClEJcLojicIiMjjJRxh3smxGRGRh2revA0ZXSNwPXC8igVSVtZXAEzN9/nKXi2GPQq+uiCVtjvbPLXwffrGLm771FG2dYQS4/sIWvnDD+VkPX0uEhko/LQ166nA52rhmHre/5hzmVQcZGEkwrzqY82We021FnvVhnCLyPWAj0CQih4B/BDaKyPm4vef9uKctY4x5QUR+ALwIJIGbi3kFRKFkY9hj4nDD7u4wC2rG11Wa67hXpkMac61oNhxL8vlHd/NIqzsJ2Vjp56PXrGHd0vppHjkzWhT99JHvd7UZbcQoVlqOcmYm28xxqG+Ehkrf2Nlv4A5rzKsO8r0bL87Ka0y2YWQk7obvbCuavXhkkDsfaOXoQBSAS1Y08uGrVlNbkd2eaWXADd58laRUZWPu5ShV+dja1s377nuGSNwm4LVoqgpQE3ILwvQOJ6jwe7My7pVJSczhWJLuWdZ1sB3DfU8e5Ou/3o9jwOcR/uoVZ3Ld+WdkdaIt4PPQqEXRVY5pAJep9GGAKr+H48NxhuNJvJaQtA1HBtzyi01VAZK2w7zqYFZWc0y3QWUwmuBYODar5+4Jx/jkg6082zEAwLLGCv7hT85meVP2TqvQougqn/S3rAxNXNmwp2eIpG3wWRYG3NqzDhwbiuH1CCvn18xquGEyUy3lGYgkOD48u/D95e5jfObnOxmMuhs0rjv/DP5ywwoCWeqhalF0VQgawGVo4jBAwjbYjhmb7LIscByI2XCob4TrzpvxgpZTOtVSnjdd1DKr8I0mbO7eupf/e+4oADVBLx+6ajWXntWUlfaKCLUhH3VaFF0VgAZwGUofBhgcSeCkgtfg9vTGgljcnXQ/fPowL11cl5XZ38lWatxwYQvnLJr5LrS93UPcsaWVA73u8MW6JXXccs0amqoCc24nQFXQS0OFX+vyqoLRAC5D6cMAx4ZieASSxp2WHQ1jSd2vJuTL+rlxo0t5jDH0hGMMzbCugzGGHz1zmHu2tZOw3boX77p0GW+8MDsF0yv8XuorfQS8OsGmCksDuIicav3sTLcKpw8DxG0HyxI8jsHnsYgmHUTcAK5J9ZJzsd/dGEPXYIxIPLPwfaK9l/ue7OBwf4RY0hkb611UF+LWTWtYs6Bmzm3yey0aKwNaFF0VDQ3gIvGFR3bxpa17SToOAY+F7Th8/P4XeMOhfn749OEZbRUeHQa468FWko7BGAh6LebXBDk2FBurJTEq2/vdRyuaRROZ7aV5or2Xzz+2m4Tt0BdJjA2RrFtSx+3XnTPnWgs+j0VdhY9q3b2miowOfhWBrW3dfGnrXhzj9lJtA8eHEiRsm6/+at/YhFo4mqRzIMqR/hHed98z05ahjCQcFtQE8FmCbdylZ36v4BioDnpzUsXNdtzXyTR8Ab77xEGGokmODcWxHYMl0Fjpw3GYU/hacqIouoavKkYawEXgnm3t2I7BI4IgWCKIwEAkwXDcJuTzMDiS4MjACEnb4LFgOJ7k4/e/cMoQHl0J0VQVZFF9CL/HwjEG24H3X34Wy5uqsr7fPWk7HOkfIZ7MvK7Dwd4ILx4dJJwaJw56LZY2VNBQ6adzcGRW7Rhd2dDSUEFthU+XlamipUMQRaCjL0LAa5G0DaNZIQIx26HS72UkYXNsKIaFYFluDzbotcaK5kwWnukrIaqD7ttvYwwDIwned8Uq3pflnyGedCuaJZ3MwtcYwwN/6ORLj+8hmRpyaKjw0VjpR0QYSdgsqJl5MZ2qgJf6Sr/W5VUlQQO4CLTUV5C0HY4Px8Fxw9c2Bq9l8e7LlnPv9gNE4jYGEMd927KwtuKUk2db27oZHEnQORAdt+04V7VNY0mbzoHMi+qEowk+8/Autu1yC6bXhnx4LKHS7wFxx6STjuH6C1umeaYTgj4PDbp1WJUY7SYUgZs2rMDvdWsPeD1C0jFYIty88UxeurjOPQEjdV9jwDbQNxSbNFBHd8FVBjwIELcdjgyM0BOO5qS2aTThlpPMNHyfO9TPe+59aix8N6xs4pvvuJAPX7maxsoA4WiSxsoA7798JRetaJj2+XweiwW1Qc6oC2n4qpKjPeAiMFWZyRs2b5803AZiNmYwyj9sOnvcdXftrM3gSBIHMKlVED1DcepC3rE6v9kY843Ek3QNZlZUx3YM3/rtAb79uwNjQyg3v/Isrj13ASLCRSsaMgrcUVoUXZUDLUdZRNLX+1YH3FUKu3uGxwI4vScM4BWoCHgZjttU+j28+7LlfPVX+8bW0Fqp9b62cb8O+TzEUuuA51f5wbJmfQTRUCxJT4YVzToHotz5QCsvHBkE4KzmKm7btJYljTMfDhFxl+PV6tZhVdwy+uXUAC4S6QV0krbD4X63zq1HIGafCGDEHYaYTHpAj/7XT79rILUSIpEK9KUNIbwea9J6vVOZSUWzx9u6+ewjuxiOucvSmqsCiBjOqK3g+gtbZtTr1a3DqoRkFMD6m1wk0gvoHBuK47EEjwikLaEynBy+AmM729JvMpx8aJ+VWg88+ozHhuIzPoKoPxLPKHxH4jafeqiNf9nSynDMpirgpaHCR9BnURvycXw4xucf280T7b3TPlfI72FRfYh51UENX1VWdAy4SKQvG4vbztjRN7Yx1AY8DKR6kKNhOzosbMb+5wSv5d4+cejYTo0HA2MTdOCu3336YB+XfeqxKYckeofj9Efi0/4sOzvD3LGllcP97jrei5Y3MBxNMhhNjB37PVop7b4nO07ZC/Z5LBqr9NRhVb60O1EkWuorGEntHvN73DXBsaRD0jYkDVT6LTyWu0mjappi4aPnW058DzR68OVoVTS/x2JwJMHh/igC47Y6T9zg0ROOTRu+jjHc98RB/uZ7z3C4fwSfR7j5lWfyyde9hGPDMYK+8b9uQZ816WYLjyU0Vrk72DR8VTnTAC4S6SceV/o9bg0HwGO5PdVo0vD+y89i7yeu5bl/umrK57JSPWCDG8Ihr4wbovCkkrmpyk9X2B1rXlAbnPRUZLeoTpRwNDHlax4fivGRHz7H5l/uw3YMSxoq+PKb1vH6dYsRERbWhIgmxm/SiCaccZstRNyi6C31FdSGdAebKn8awEVi3JHY0SR+j+Cz3N5s0jZ4BB58vnPs/lMtAAimlVm0LMGyLJqq/FT6PViWsGp+NWc1V7ohbWBRXXBcrYTRDR6jRXWGpykn+Zu9x3j3vU/x1MF+AP7kpQv5ypvXcea8qrH7XH9hC0nHMJKwMZiTNltUBbwsrg/RUOnX1Q3qtKHv7wpsslKTt/3keTwCRwdi+L3uuK/jGHZ1D7G1rZuNa+ZRFfAyFEueNM4LJ8Z2AXypM+D6RxI0Vvp5aVPVuOOHbti8ne5UL3jUSMJmUV2Io4NRYlMU1YklbO7Z1s6Pnz0CuAV+PnjlKjasbD7pvhetaOD9rOS+JzvoHBxhQU2I6y9sYcPqZt3Bpk5bGsAFNLr0LJ60xyqdPX2wj+YqP13hOCKMFSAXwOdhrPbDq9Y086Nnj076vAn7RCobQCzAgb5Igrsm7ISb7AiheNLh9esWTxm++44Nc8eWVvYdGwbgvMW1fOzatTRXn/q0ivTNFj6PRX2lf9rxbKXKmf72F9A929qJJ22OD8exELxjZSOjk/ZsxYanD/axta2bJ/f3Tfv8Pgu8lhC3HXyWUBHwnrS6YeIuvDPqQrxh3WJetqx+0uc0xnD/749w9y/aiScdLIF3XLqM6y9cMrZyYyqWCPUVfmpCevilUhrABdTRFyEcTY5VOQPAcSYNX3B7s0nb4X3fe5rB2PT1dm0DK5rdcdhIPMm86uCk9xs9QiiWtOkaiJ2yotlAJMGnf76T3+w9DsDC2iC3XruWs8/I7LSKmpCP+gp/RkGt1OlAA7iAWuorONI/4q7Ntd2xXmMm38U2yjZkFL7groQYHImP7XabqhBPNGHTNXjqojpPH+zjkw+2cXzIXYp2xdp5vO9VKzMaQgj53UplegabUuNpABfQJSsa+G2725sU3PA1uCscsrVDvKNvhFXzqviHTWefcqvxz54/yt2/aOfowAgLU5Njo2O1Cdvh67/ez/ef7MDgrpD42ytW8uqz50/72rqRQqmp6f8zCui37b3UV3jpjyTH1uyC23PNZggPRRPcs62d237y/Ek73R76w1H++acv4rWEmqB3bIvw+1nJovoQd2xpZWdXGIA1C6q5ddNaFtVNXSjdY7nreWuCOs6r1FQ0gAuooy/CoroKaoJJOgejMzrKJ1OOgUMDMZIGmqoC4w71fNmyer6yrR2vJeO2CEfiSb68dS89qZrDAtxwUQtv/6NlU9ZiEHFDvE7HeZXKSM4CWET+C/gToNsY85LUtQbg+8AyYD/wRmNMn7jdpM8D1wIR4O3GmKdz1bZi0VJfMbYG1zHuZotkqtebYX3zjHUNxghHkzRVBfB5hC89vodPveGlHB0YoSbo5dhQjL5I4qRx55qgl+aqAI+2dfPikfApK5hNdRTQZGuds1GPWKlSl8udcN8Arp5w7RbgUWPMSuDR1PcA1wArUx83AnfnsF0Fs7Wtmxs2b+eyTz3GDZu3c8mKBhK2oaM3QiJV8yFX3BUU7onFsYRNR+ooo4U1IboGo/ROEr7NVX5CPg9x2xk3PJFewSzo83BGXYh5NcFThu/H73+B7nB0yloTSp2OchbAxphtwMRag9cB30x9/U3gtWnX7zWu7UCdiCzMVdsKYbIg+uHTh1lUGyD7Aw8nE9xtyWKgeyg2VoPh+gtbGIyevKpCcMtV+r0WIZ8HwR2m8FrCfU924PdmdhRQepnNyWpNKHU6y3ctiPnGmNHtW53A6FT6IqAj7X6HUtdOIiI3isgOEdnR09OTu5Zm2amC6IkD/Xlrg2McDIakfaIGw9Kmk0+l8Ap4PW6veWIFs5DPQ89QlMX1FRmtbujoi4yNL6c/x2SHiSp1uilYMR7jHsUx4zfdxpjNxpj1xpj1zc0n1xwoVhODKBxNcLR/JOPDLGdisukvg1sPWERY1ljJRSsa2Larh/fc+9TYfTwCfo/g8ViY1EqMaMJhOJ6koy9C+7EhDhwfntE5bOllNkfl6nRmpUpNvgO4a3RoIfV5dCDwMJB+Bvni1LWykR5E4WiCI/3RsaOBsm2yZ/VZsLA2RGNVgLdfsozP/HwX//R/LzIUSxLwWmMnaxgMdmo33uWrmxmKuTUqkraDJYJtoGcolvEYbnqZTWPcz7k4nVmpUpTvAL4feFvq67cBP0m7/lZxXQwMpA1VlJyJk21b27q5ZEUDh/pGePHIAPuPR4jbbrH1Cm9+lmsZA42VAd6wbjFf/fU+tvzB/ed92dJ6vv2ui3j7JUsJeD3YDgS8Ht528VL+5XXn0lzlx+e1QASfx2JRXYjakC/jMdxxZTZHEsyrDs7o/DmlylnODuUUke8BG4EmoAv4R+DHwA+AJcAB3GVovallaP+Bu2oiArzDGDPtaZvFeCjnB+57mh///ujYW/iaoBevx+1hJh2HvsjUtXVzwT3o07CwNkhXOIbtGLyW8O4/Xs4bXrZ4rOIawBPtvfxgRwed4ShLGyrZ3R1mQU1w3IYKYwwDIwl++ZHL8/6zKFUiMupZ5WwdsDHmhlPc9KpJ7muAm3PVlnz5wiO7xpWIdAz0jySxAL/XyugI91xI2gYRODLgrjluqvRzx+tewqr51ePut2N/L//x+B4CPouGCj/d4SjhaBKfJ0ZT1YlCPjqGq1R26E64LPrqr/YB4w4yxhhwcHu/6We15TOK009TrvR7WFgbGhe+lgi1IR//+/RhAj5rbHVDhd9LfYWP3uEEFX7vWL1gHcNVKjs0gLNoOO5u2x1X2CHFa1nYpyjzmA8CeD1CNGHT1jXIE+29XLSigeqgj/oKH16Pxe7uMNGEQ9x28HssmqoCNFUFSNoO86qDHOqLsFh3simVNRrAWVTp9zAcT+I4jAthwT2uJxlJjB22mW+jB3Fa4vbQv/D4bm6vOocrzlkAuBOHQzE7tSVaxnbNNSb9rJxfM+4YI6VUduihnFn07suWA4KV+lcdfdv/2vMX8paLl5LfgYfxnNSqawMsqA4S8nn42q/3j91+z7Z2Gip9CHLiGCPcY4x0uEGp3NAecBa974pVgDsWPBy3qfR7eNWaZjoH4zzw/F53GMCCHBQ9m5TfI1QFvPRGEjhA0CM0V4eoCfkwxozbjdbRF6GxMkDA66EnHJvyGCOlVHZoAGfZ+65YNRbEo/UffB4hkXTGaj54xD3ZItc8lhDyewjFkyQcOHPeiYm3iSsZRiuzVQd9Y0fUT3WMkVJq7nQIIofS6z+kjwcb3MMyc20k4XC4P8pI0pB0DM8fHqBzIDLpbjTdsaZU/mkA51B6/YfRjQwGdzw2maNtyFMxQM9Qglg8edJuNN2xplT+6RBEDo2+ra/we/FZkpPCO7PRG0lOGqyjpyMrpfJDe8A5lP623ingGuCJIonMTlVWSuWW9oBzaOOaedyOOxa8/3iEgAdi+Zh9Y+rddqV4TqYea6TKkQZwjkwMjJXNlew7NpS3158q5hfVFtfKhunCNX01SfqxRreDhrAqaToEkQOTHT90pH+EWB7f+Yd8FovrxgetADUBD3e89tz8NWQamZwZp8caqXKlAZwDkwXGcDy/465x2/CrW17FN95+IZesaKSlPsTFKxr5wg3riqrXmEm46rFGqlzpEMQcnOqt8+7uMOGRBPHUeK/XI3nZeJEukDqhuNhXNnT0RagLjT/iaGK4pq8mGaUlMVU50B7wLJ3qrfMXHtnFwEhi3GRbIt/pi7vluRSOfs/kzLjpNolMdgKJUqUgZydi5EMhT8S4YfP2k3plkXiSnnCM4Vgy7z3edD4LqoJebAdqQr6iXjWQPsGWXm944iaQ0XcbE0tiZvp4pfKssCdilLtTvXUeiiUp9H6LhMPY0UdJ28F2nGlXDZxqOCXXy7/Sl+pNVW/4VEMp47Z74xaRj8ST3LOtXQNYFT0N4Fk61bikiJyoQ1kEEo7h+FCCxipOGUqnWub1hkP9/PDpwzlf/jWXcepMxpCVKlY6BjxLk41LDo4kima78aikbUjYDkcHYjx9sG/S8dFTrUT46q/2Ff3yr0zGkJUqVhrAszSxeI3fY2FwT5woJqk67IC7A27iGls49TKv4bhd9Mu/8lXFTSf6VC7oEMQMbG3r5q4HW9l33A2gFU2VfOTqNWxcM48bNm8nbjscH4oXuJWTE2B+dRBvqgeb/pa/pb6CfceGCEeTY+fBVQe9VPrdSa1iXv6V6RjyXOhOPJUrGsAZ2trWzd//8Pf0RxJjvdydnWHe860d1IZ8hKNJAp5CHjo0teqAZ9KTMAAuWdHAE/t7scTtwcdth56hOK956QKeOjhAJJ4s6hORc73WWSf6VK7oEESG7tnWzlAsiUcEj2WNHTefsA2RWBIBBmPFU/FsosGYTeuRAY4NxU7qwf62vZfaoBfbMcSSBtsx1Aa9dA7GtUYwuhNP5Y72gDPU0RfBdtwTg8FdXTAqknDwlcCfsqSBrsEYb7poCXBi6dnv9h3HMeDzCH5LMAbC0SS7u8NFv5MuH3QnnsqVEoiN4tBSX4EnFU5J++SebqJ4O7/j+DzCb9t7x+3kG5W0DY4DlggIxPN1emiR0+OaVK5oAGfopg0rqAp4sY0Z1/stBaP1fy1xx6gP9UVOGtcE97aE7eCkfj6/p8iWdBSIHtekckWHIDK0cc08/u0N53Hbj57j0ECs0M05pdHITP8TMbovxMI9KXlxfcW4DQwhn4dYwsbBPa/O6xGqgz6WN1XlseWZKVRhdh2KUbmgPeAZ2LhmHlVB3/R3zLFKv4eAR/BO+K83Gr5LGytoqPCNfS+ARwARqgJebtqwYtwGhqaqAGIJHkuo8FksqA3i93qK7i12JrWDlSolBekBi8h+IAzYQNIYs15EGoDvA8uA/cAbjTF9hWgfTN7TAtjZlb9TLU5lRXMVbZ2DeD0WGIdkqoc72uvdfzyCzyO89vyFtHUO0X5sGIDljRXccs3asZ7cx+9/gZ5wlHA0ie0YDBD0WsyrDuZkLe1ce666HEyVm0L2gF9pjDnfGLM+9f0twKPGmJXAo6nvC2KyntY//OR5PvajPxTFOt8XjwyQsA3RhINtoDbgOek+jmN4bGcPxhiaqwOsW1I/Lnw3rpnHG9Ytoi+SIJq0CXotFtQEqK8M5Gwjw1x7rrocTJWbYhqCuA74ZurrbwKvLVRDJtZGCHg9iMCRgWjB/8G8FuNKXRrcNb6j/B7LDSkRBkaStHUN0TsUY//xoZNC77ftvSyuD3H2wlrOnFdNU1UwJ7UesnWkUKZ1H3TbsCoVhcoTA/xcRJ4SkRtT1+YbY46mvu4E5k/2QBG5UUR2iMiOnp6enDRutKflOIZ40iFpOwQmDrgWSNJxx3TT1yeM5rEvNY5rO2ZcUSDbwPGhBAnbHhd6u7oG6RyI0tY5SHvPEIMjiZz0KLPVc81kOZiOE6tSUqhUucwYsw64BrhZRDak32jcKvGTvts3xmw2xqw3xqxvbm7OSeMW1YUIRxMkbIfRgvXhqLvbrShWxk5M4JTRf7Ckc6KV7vZiQQQGIomx0Nva1s1QzCZuO3hESNqGIwMjk+6Um6tsVSzLZDmYHuCpSklBJuGMMYdTn7tF5EfARUCXiCw0xhwVkYVA3rsstmPoHY7zuvMX8fnHduMYm4BXONQ3QjRZDKO/rlOVG046BrDHFYQf3bknAjHbGQu9e7a101Dp4/hQAgOIBTjQF0lwV5ZXP9y0YQUfv/+FrNSUmG45mNYHVqUk7z1gEakUkerRr4ErgeeB+4G3pe72NuAn+WqTMYb+SJyO3gjhaIKLVjTw/stXUhv0se9YpODhe4oO76RGN6+NFtaxRDDGHZLwWtZY6HX0RWisDHBGXRBvatjCZwnVQe+Up2bMZmw1nxsZtD6wKiWF6AHPB34kbs/MC3zXGPOQiDwJ/EBE3gUcAN6Yj8aEown6hhPj3rYD+LzCwb5IQc52O3dRLe09Q0QT9tjGCNsxGR+0sbA2QHc4TnXAQ8I2xJIOHku4eeOZY6E3Wt+gOuijOrW2ORJPMq86OOlzzrUkY742MmSzt61UruU9gI0x7cB5k1w/DrwqX+2IJmyOD8eJTegtJW2Hb/xmP997oqOgS87ithuaQY81tu7XMmZsEu5UbROgqcoN0eGYTUOlf9IauTMNqlJZg5uP+sBKZctptxU5nnToi8QZjiVPuu1w/wh3bmmlrTMMuIVr8n2kfJXfwhi36lrSMTRVBQB3eVkkbiNAMBWYkzFAe88QTVV+vCGLX37k8knvN9OgKqWxVd02rErFaRPAtmPoi8QJR5NjKxvSPfxiF59/dPdYyAF5D99LlteDWBzqi7C8qZKeoRhejzuGWx30Mhy38VpgpumbRxI2B3pHqPB72NrWfcowmklQaUlGpbKvOBa35pAxhoFIgo7eCIMjiZPCdziW5BMPtPLJB9uIxG33KJ6AJ2/DD36P4LWEpio/l5zZ5LYZqKvw89aLl45NXC1vqqKlPoTXsrAdgyVufYfJJudGf8RE0s7aGlgtyahU9slkvcFSsX79erNjx45T3j4US9I3HCcxSf1egBePDHLnA60cHXBr4l6yopHwSIJd3WHiOej9TlaprNLvoakqQCxp0xdJsLg+NG5MNn21QPpEWNJ2ONzvttsxBuHEDrnRcEaEJQ0VzKsO8r0bL55z+0frOejYqlLTymjhUlkG8Kkm2EbZjuF7TxzkG7/ZP3YSxF+94kyuO/8M3vTV33EsHMv66gefJdjGjK3R9VmACGsW1ADuuG00aXP2wtqxxxwbijIcs6kJ+cYVBBoNwaqAF2MM+45HENxTOnyWICI4xuC1hOVNlQyMJE45FqyUyomMArisxoCnmmAb1ROO8ckHW3m2YwCA5U2V3LZpLcubKlJCOZ0AAAukSURBVAFYWBOibziOncUEHl2P6/VYGAyxhAMi+D0nRoBiSYdA2vfhaIJj4TgGWNJQcWLZ12vOOak3m7791nHc3rAx0Fwd0HFapYpYWYwBJ22HnnCMQ32RKcP3l7uP8e57d4yF72vPP4Mvv+mCsfAFuP7CFqoC2f+75GBwHPfD5xEcA9VB79h4qscSgj4P7T1DtHUOcrA3gmMMAa817Zba0Y0OyxoqsFPDEQtrA3gs0XFapYpYyfeA+4bjDIwkcKYYSokmbO7eupf/e86t9VMT9PLhq1fzR6lJr3SXnNXIp6peymcf3sXOznBWaj94LWFhbZCucIykDavmVXHNSxbw2/besfHUly6q4f7nOsd2sCVSP06F/0QRm6mWfY2uaEgfp81FXV+lVPaUdAAnbHfIYSp7u4e4Y0srB3rd4Fq3pI5brlkztr42nddyT4NY2ljJq89ZAMANm7fz2/bjc2qnx3J7vBe01I8LxPel3eeGzdtprvITjiaJ2w5WagQpEj8xjp3JcIKugVWqdJR0AE81SmuM4UfPHOaebe0kbIPHEt556TL+/MIW99TfCXwei4W1QfeUiTQdc9ho4LXcymqOYdpJsI6+CE1VAZpTW4HD0QSH+0aIJd2KbLqlVqnyU9IBfCp9kTj/+tBOfrevF3BD8NZNa8ZWHDzR3st9T3ZwdHCEhTUh3nzxkrGTgkdmcL68JfDyZfX8dt+Jk5MCHli14MRKhqnqK6SbuNGhOuijqdpmOGYzMJLQZV9KlaGyC+An9/dy14Nt9EUSAFx1znzee/lZY8H2RHsvn39sN15LqAl66YvEuP2nLzAYnXzJ2lSuO28hn7t+3dj3o6sRZlMIZrLaDD6Phy9c/1INXaXKVNkEcDzp8LVf7eO/nzoEuBscPvDqVVw+Ibzue7IDryWEfB4sS6gOejjYOzKj17Lk5PCFuRWC0SIySp1+yiKAD/ZGuGNLK3u63ROLz15Yw62b1rCwNnTSfY8OjlAT9GJZwt6u8Nhqg0x5LGHvJ6495e1zmQTTCTSlTi+lHcAGtjx3lC89vodo0l058BcvX8JbL1mGx5p8I8rCmhB9I3GOHpt5+ILbs1ZKqWwo6Y0YR/pH+MzDu4gmHeZVB/jMG8/jHZcuP2X4ArzzsmUYA/FZLvB992XLZ9lapZQar6R7wOFYkipgw6omPvjqVWMnO5xKfYWf15y/iJqgj7d/48kZvZYAH7hiJe+7YtXsG6yUUmlKOoBF4O+vXMU1L1mATLK2N11jVYDaVEHxmYyzBr0WFyypz0o1MaWUSlfSQxDLGiu59tyF04Zvc/WJ8B21Zn7lKe49XnXQq5sflFI5UdIB7PdO3XwRYX5NcNKhiYc+sHHaEF67oJpPv+E8XZmglMqJkh6CmIqVCt/QFKsWHvrAxvw1SCmlJijLALZEWFAbJOjTJWNKqeJVdgHssdzwDXg1fJVSxa2sAtjnsZhfE5x2bFgppYpB2QTwqcpJKqVUsSqLAA74PCyoCU65A04ppYpNyQdwyO9hfnUQS8NXKVViSjqAPSIsqAlOuxFDKaWKUdENmIrI1SKyU0T2iMgtU93XY4mGr1KqZBVVAIuIB/gScA1wNnCDiJxd2FYppVRuFFUAAxcBe4wx7caYOHAfcF2B26SUUjlRbAG8COhI+/5Q6ppS/7+9+42x4qrDOP59BC01qJUWEQPpUsU/NSJSsJCqJbQmhKDygoibGtuEhJjUBBOrodY06hvtG+v/phibGiRKtCYS2kgrBVObWIqAQKEtGPSF1rI0FP81TcHHF3MWb3G3yy7snrv3Pp9kcmfOnLlzfvfFb8+cnTkT0XHaLQEPSdIaSbsk7err66vdnIiIEWu3BPwXYGbL9oxSdobt9bbn254/derUMW1cRMSF1G4J+HFgtqRZkl4DfALYXLlNERGjoq3uA7Z9StJngK3ABOAe209UblZExKhoqwQMYPsB4IHa7YiIGG3tNgQREdE1koAjIipJAo6IqCQJOCKikiTgiIhKZLt2G0ZMUh/w59rtGEWXAcdrN2KMJebu0clxH7e9dKhK4zoBdzpJu2zPr92OsZSYu0e3xt0qQxAREZUkAUdEVJIE3N7W125ABYm5e3Rr3GdkDDgiopL0gCMiKkkCjoioJAm4Ekn3SDom6UBL2RRJD0k6XD7fWMol6dvlTdH7JM2r1/KRkzRT0nZJByU9IWltKe/0uCdJ2inpDyXur5TyWZIeK/FtKnNgI+misn2k7O+p2f7zIWmCpD2StpTtjo95OJKA67kXOPtG7XXANtuzgW1lG5q3RM8uyxrgrjFq44V2Cvic7SuBhcDN5a3XnR73i8AS2+8F5gJLJS0E7gDutP024ASwutRfDZwo5XeWeuPVWuBQy3Y3xHzubGeptAA9wIGW7aeA6WV9OvBUWb8b6B2o3nhegF8CH+6muIHXAruBq2meAptYyhcBW8v6VmBRWZ9Y6ql220cQ6wyaP6hLgC2AOj3m4S7pAbeXabafKet/A6aV9Y57W3S5xHwf8BhdEHe5FN8LHAMeAv4IPG/7VKnSGtuZuMv+k8ClY9viC+KbwBeA/5TtS+n8mIclCbhNuekKdOQ9gpImA/cBn7X999Z9nRq37dO259L0Ct8PvLNyk0aVpOXAMdu/r92WdpYE3F6elTQdoHweK+VDvi16vJD0aprku9H2L0pxx8fdz/bzwHaay+9LJPW/Fqw1tjNxl/1vAJ4b46aer2uAj0r6E/BTmmGIb9HZMQ9bEnB72QzcWNZvpBkj7S//VLkrYCFwsuWSfdyQJOCHwCHb32jZ1elxT5V0SVm/mGbc+xBNIl5Zqp0dd//vsRJ4uFwZjBu2b7U9w3YPzdvNH7Z9Ax0c84jUHoTu1gX4CfAM8BLNWNhqmjGvbcBh4NfAlFJXwPdoxg33A/Nrt3+EMX+AZnhhH7C3LMu6IO45wJ4S9wHg9lJ+BbATOAL8DLiolE8q20fK/itqx3Ce8S8GtnRTzOe65FHkiIhKMgQREVFJEnBERCVJwBERlSQBR0RUkgQcEVFJEnC0LUm3ldnD9knaK+nqYRw7V9Kylu2bJH13GMd/cYCyFZIsqaOfYouxkwQcbUnSImA5MM/2HOB6Xj4vxCsdO5Fm1rFlQ9V9Bf+XgIFe4Lflc7DzRpyzJOBoV9OB47ZfBLB93PZfJS2V9KSk3WWu4P55Zr8saYOkR4ENwFeBVaXnvGqwk0jqlbRf0gFJd5SyrwMXl2M3lrLJNA+SrKZ5sqv/+MWSHpG0GThYyj5Z5v/dK+luSRNK+V2SdrXOCRzdLQk42tWDwExJT0v6vqRrJU0CfgB8BLgKePNZx1wJXG+7F7gd2GR7ru1NA51A0lto5p1dQtNjXiBphe11wAvl2BtK9Y8Bv7L9NPCcpKtavmoesNb22yW9C1gFXONm8p3TQP933GZ7Ps2TcddKmjPynyc6QRJwtCXb/6RJsmuAPmAT8GngqO3Dbh7h/PFZh222/cIwTrMA2GG7z80UiBuBDw1St5dmUhnKZ+swxE7bR8v6daXdj5fpJ6+jefwW4OOSdtM8lvxumj8Y0cUyZhVty/ZpYAewQ9J+/jdZy2D+NRrtkDSFppf8HkkGJgCW9PkBzivgR7ZvPes7ZgG3AAtsn5B0L838B9HF0gOOtiTpHZJmtxTNBZ4FeiS9tZQN+M+w4h/A64Y4zU6aoYDLyjhtL/Cbsu+lMnUmNLNzbbB9ue0e2zOBo8AHB/jObcBKSW8qcUyRdDnweppEfVLSNJrXLUWXSw842tVk4DtlGsdTNLNkrQF+Dtwv6d/AIwyeZLcD68owwNdK2U2SVrTUWUjz/rntND3X+233T4+4HthXhgz6x4pb3UeTsF82vmz7oKQvAQ9KehXNbHc32/6dpD3AkzR3czx67j9FdKrMhhbjlqTFwC22l9duS8RIZAgiIqKS9IAjIipJDzgiopIk4IiISpKAIyIqSQKOiKgkCTgiopL/Ai2n24C4n0MsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb5fe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相关度较大\n",
    "# 拟合的曲线 y = 0.6 * x\n",
    "sns.lmplot('SqrtLotArea','LotFrontage', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x11cbd6438>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cbd6588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('SqrtLotArea', 'LotFrontage', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把空值拿出来\n",
    "filter = train['LotFrontage'].isnull()\n",
    "train.LotFrontage[filter] = 0.6 * train.SqrtLotArea[filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['LotFrontage'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. MasVnrType 和 MasVnrArea 的填充\n",
    "\n",
    "砖石镶板种类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getNullCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11c8a2c50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c9e7860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MasVnrArea 和 SalePrice 的关系\n",
    "# 基本没什么关系\n",
    "plt.scatter(train['MasVnrArea'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4774930470957163"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['MasVnrArea'].corr(train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cecf128>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c88ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 什么都没有价格也不低，相关性不太强\n",
    "sns.boxplot(train['MasVnrType'], train['SalePrice'], data=train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TypeError: unsupported operand type(s) for /: 'str' and 'int'\n",
    "# train['MasVnrType'].corr(train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>SqrtLotArea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BrkCmn</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrkFace</th>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>10</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>11</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>77</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>188</td>\n",
       "      <td>40</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stone</th>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Id  MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  \\\n",
       "MasVnrType                                                                   \n",
       "BrkCmn       15          15        15           15       15      15      0   \n",
       "BrkFace     445         445       445          445      445     445     10   \n",
       "None        864         864       864          864      864     864     77   \n",
       "Stone       128         128       128          128      128     128      3   \n",
       "\n",
       "            LotShape  LandContour  Utilities     ...       PoolQC  Fence  \\\n",
       "MasVnrType                                       ...                       \n",
       "BrkCmn            15           15         15     ...            0      7   \n",
       "BrkFace          445          445        445     ...            2     80   \n",
       "None             864          864        864     ...            4    188   \n",
       "Stone            128          128        128     ...            1      6   \n",
       "\n",
       "            MiscFeature  MiscVal  MoSold  YrSold  SaleType  SaleCondition  \\\n",
       "MasVnrType                                                                  \n",
       "BrkCmn                2       15      15      15        15             15   \n",
       "BrkFace              11      445     445     445       445            445   \n",
       "None                 40      864     864     864       864            864   \n",
       "Stone                 1      128     128     128       128            128   \n",
       "\n",
       "            SalePrice  SqrtLotArea  \n",
       "MasVnrType                          \n",
       "BrkCmn             15           15  \n",
       "BrkFace           445          445  \n",
       "None              864          864  \n",
       "Stone             128          128  \n",
       "\n",
       "[4 rows x 81 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只有8个空值，有高频填充即可\n",
    "# 进行聚类，看它 个类 有多少个\n",
    "train.groupby(['MasVnrType']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MasVnrType  MasVnrArea\n",
       "1       None         0.0\n",
       "3       None         0.0\n",
       "5       None         0.0\n",
       "8       None         0.0\n",
       "9       None         0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把None的过滤出来\n",
    "# 看 MasVnrType 和 MasVnrArea的相关性 \n",
    "train[train.MasVnrType=='None'][['MasVnrType', 'MasVnrArea']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11c8dff98>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d0af128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点图 + 回归\n",
    "# 斜率比较缓，相关性比较低\n",
    "sns.lmplot('MasVnrArea', 'SalePrice', hue='MasVnrType', data=train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter=train['MasVnrArea'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getNullCount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(filter==True).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.MasVnrArea[filter]=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter=train['MasVnrType'].isnull()\n",
    "train.MasVnrType[filter]='None'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "后续在EDA的特征工程中，可以使用如下填充办法：\n",
    "\n",
    "分类进行预测MasVnrType，使用回归MasVnrAre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alley           1369\n",
      "BsmtQual          37\n",
      "BsmtCond          37\n",
      "BsmtExposure      38\n",
      "BsmtFinType1      37\n",
      "BsmtFinType2      38\n",
      "Electrical         1\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c6bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Electrical的填充\n",
    "\n",
    "电力系统，保险丝、空气匝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d0a4240>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d09dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 先看和真正目标的 有多大的相关性\n",
    "sns.boxplot('Electrical', 'SalePrice', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>SqrtLotArea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FuseA</th>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>12</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FuseF</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FuseP</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mix</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBrkr</th>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>73</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>256</td>\n",
       "      <td>52</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "      <td>1334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Id  MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  \\\n",
       "Electrical                                                                    \n",
       "FuseA         94          94        94           94       94      94     12   \n",
       "FuseF         27          27        27           27       27      27      4   \n",
       "FuseP          3           3         3            3        3       3      2   \n",
       "Mix            1           1         1            1        1       1      0   \n",
       "SBrkr       1334        1334      1334         1334     1334    1334     73   \n",
       "\n",
       "            LotShape  LandContour  Utilities     ...       PoolQC  Fence  \\\n",
       "Electrical                                       ...                       \n",
       "FuseA             94           94         94     ...            0     23   \n",
       "FuseF             27           27         27     ...            0      2   \n",
       "FuseP              3            3          3     ...            0      0   \n",
       "Mix                1            1          1     ...            0      0   \n",
       "SBrkr           1334         1334       1334     ...            7    256   \n",
       "\n",
       "            MiscFeature  MiscVal  MoSold  YrSold  SaleType  SaleCondition  \\\n",
       "Electrical                                                                  \n",
       "FuseA                 2       94      94      94        94             94   \n",
       "FuseF                 0       27      27      27        27             27   \n",
       "FuseP                 0        3       3       3         3              3   \n",
       "Mix                   0        1       1       1         1              1   \n",
       "SBrkr                52     1334    1334    1334      1334           1334   \n",
       "\n",
       "            SalePrice  SqrtLotArea  \n",
       "Electrical                          \n",
       "FuseA              94           94  \n",
       "FuseF              27           27  \n",
       "FuseP               3            3  \n",
       "Mix                 1            1  \n",
       "SBrkr            1334         1334  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计下，用众数填充\n",
    "train.groupby('Electrical').count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用众数，在朴素贝叶斯中概率也大，用其它的分类模型学习它的数量已经决定它的可能性了\n",
    "# 数量上已经不均衡了\n",
    "filter=train['Electrical'].isnull()\n",
    "train.Electrical[filter]='SBrkr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Alley 小路 的填充\n",
    "\n",
    "EDA 后半段考虑删掉\n",
    "\n",
    "80%以上是空值，可以删除了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grvl    50\n",
       "Pave    41\n",
       "Name: Alley, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 小巷的数据分布，只有2个\n",
    "# 推断 NA不填 本身就是None\n",
    "train['Alley'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train['Alley'].value_counts() 与groupby().count()效果一样？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119e7df98>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119e7dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot('Alley', 'SalePrice', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 大概率都是None\n",
    "train['Alley']=train['Alley'].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BsmtQual          37\n",
      "BsmtCond          37\n",
      "BsmtExposure      38\n",
      "BsmtFinType1      37\n",
      "BsmtFinType2      38\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c927748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.BaseMent 群填充\n",
    "\n",
    "TotalBsmtSF 是一个完整的关于Basement的列，可以拿出来与SalePrice相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11a06e978>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ec96a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 本身和房价做分析，相关性挺高\n",
    "plt.scatter(train['TotalBsmtSF'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2  BsmtFinSF1  \\\n",
      "17        NaN      NaN          NaN          NaN          NaN           0   \n",
      "39        NaN      NaN          NaN          NaN          NaN           0   \n",
      "90        NaN      NaN          NaN          NaN          NaN           0   \n",
      "102       NaN      NaN          NaN          NaN          NaN           0   \n",
      "156       NaN      NaN          NaN          NaN          NaN           0   \n",
      "182       NaN      NaN          NaN          NaN          NaN           0   \n",
      "259       NaN      NaN          NaN          NaN          NaN           0   \n",
      "342       NaN      NaN          NaN          NaN          NaN           0   \n",
      "362       NaN      NaN          NaN          NaN          NaN           0   \n",
      "371       NaN      NaN          NaN          NaN          NaN           0   \n",
      "392       NaN      NaN          NaN          NaN          NaN           0   \n",
      "520       NaN      NaN          NaN          NaN          NaN           0   \n",
      "532       NaN      NaN          NaN          NaN          NaN           0   \n",
      "533       NaN      NaN          NaN          NaN          NaN           0   \n",
      "553       NaN      NaN          NaN          NaN          NaN           0   \n",
      "646       NaN      NaN          NaN          NaN          NaN           0   \n",
      "705       NaN      NaN          NaN          NaN          NaN           0   \n",
      "736       NaN      NaN          NaN          NaN          NaN           0   \n",
      "749       NaN      NaN          NaN          NaN          NaN           0   \n",
      "778       NaN      NaN          NaN          NaN          NaN           0   \n",
      "868       NaN      NaN          NaN          NaN          NaN           0   \n",
      "894       NaN      NaN          NaN          NaN          NaN           0   \n",
      "897       NaN      NaN          NaN          NaN          NaN           0   \n",
      "984       NaN      NaN          NaN          NaN          NaN           0   \n",
      "1000      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1011      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1035      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1045      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1048      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1049      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1090      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1179      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1216      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1218      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1232      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1321      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1412      NaN      NaN          NaN          NaN          NaN           0   \n",
      "\n",
      "      BsmtFinSF2  TotalBsmtSF  \n",
      "17             0            0  \n",
      "39             0            0  \n",
      "90             0            0  \n",
      "102            0            0  \n",
      "156            0            0  \n",
      "182            0            0  \n",
      "259            0            0  \n",
      "342            0            0  \n",
      "362            0            0  \n",
      "371            0            0  \n",
      "392            0            0  \n",
      "520            0            0  \n",
      "532            0            0  \n",
      "533            0            0  \n",
      "553            0            0  \n",
      "646            0            0  \n",
      "705            0            0  \n",
      "736            0            0  \n",
      "749            0            0  \n",
      "778            0            0  \n",
      "868            0            0  \n",
      "894            0            0  \n",
      "897            0            0  \n",
      "984            0            0  \n",
      "1000           0            0  \n",
      "1011           0            0  \n",
      "1035           0            0  \n",
      "1045           0            0  \n",
      "1048           0            0  \n",
      "1049           0            0  \n",
      "1090           0            0  \n",
      "1179           0            0  \n",
      "1216           0            0  \n",
      "1218           0            0  \n",
      "1232           0            0  \n",
      "1321           0            0  \n",
      "1412           0            0  \n"
     ]
    }
   ],
   "source": [
    "# 未缺失的值 和 房价做分析\n",
    "basement_cols=['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2','BsmtFinSF1','BsmtFinSF2']\n",
    "# 把为空的 筛选出来\n",
    "# 代表没有地下室\n",
    "print(train[basement_cols+['TotalBsmtSF']][train['BsmtQual'].isnull()==True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train[basement_cols+['TotalBsmtSF']]\n",
    "# [train['BsmtQual'].isnull()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>SqrtLotArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Id, MSSubClass, MSZoning, LotFrontage, LotArea, Street, Alley, LotShape, LandContour, Utilities, LotConfig, LandSlope, Neighborhood, Condition1, Condition2, BldgType, HouseStyle, OverallQual, OverallCond, YearBuilt, YearRemodAdd, RoofStyle, RoofMatl, Exterior1st, Exterior2nd, MasVnrType, MasVnrArea, ExterQual, ExterCond, Foundation, BsmtQual, BsmtCond, BsmtExposure, BsmtFinType1, BsmtFinSF1, BsmtFinType2, BsmtFinSF2, BsmtUnfSF, TotalBsmtSF, Heating, HeatingQC, CentralAir, Electrical, 1stFlrSF, 2ndFlrSF, LowQualFinSF, GrLivArea, BsmtFullBath, BsmtHalfBath, FullBath, HalfBath, BedroomAbvGr, KitchenAbvGr, KitchenQual, TotRmsAbvGrd, Functional, Fireplaces, FireplaceQu, GarageType, GarageYrBlt, GarageFinish, GarageCars, GarageArea, GarageQual, GarageCond, PavedDrive, WoodDeckSF, OpenPorchSF, EnclosedPorch, 3SsnPorch, ScreenPorch, PoolArea, PoolQC, Fence, MiscFeature, MiscVal, MoSold, YrSold, SaleType, SaleCondition, SalePrice, SqrtLotArea]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 82 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 本身没有地下室填 NA的，偷懒没有填\n",
    "train[train.BsmtCond=='NA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in basement_cols:\n",
    "    if 'FinSF' not in col:\n",
    "        train[col]=train[col].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f30198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.FireplaceQu填充\n",
    "\n",
    "火炉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11a0ba2e8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0ba898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Fireplaces', 'SalePrice', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11a0a9e10>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0a9470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Fireplaces', 'SalePrice', data=train, hue='FireplaceQu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 上图只给了5个类型，快查表中有6类包括None，推断少了一类None\n",
    "train['FireplaceQu']=train['FireplaceQu'].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3100b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.Carage 车库列群填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11a5f7860>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fTZPK5evuV68JIlda3j0osqkoLWM+j+IDiVTdGchKRQ+gLRIilcuzY8/BBX/037a5n/uCMY8lUqwt+1yzmbfSHIwxjKYdzqYcvDmUd6tXgL8qIjuAHhH5HeC3gb+b9WiKsgDUyi5WjyjOFBpcSrNFr5q/MMxu3krjSWYcEhMOec+b8zXqMkEYY/4C2AX8E3Ap8AljzH+f86iKMg/m+yg+U2hwKc0KE67HzjybeTd6LkuZjONy/GyaoWR2XuIL9W/CXQR82xjzh8aYjwHfEZEN8xpZUebIfAtbzhQaXEozRK9eO/Ns5t3oudTqf76Kdy7vcXosw4mzabJlN+W5Uq8J4mvADSXv3aDtLQsyC0WZBQvxKF7rUb/8vFp22YVgNnbmeufdjLmUM1/T0GLF9QxnUznGMv5NeCGpV4BDxphc4Y0xJicikQWdiaLUSanvbDxsk3bchiavabToLabNtfnMpZkbls3AGMNYOk8ilZvVBttsRLpeN7QhEXlP4Y2I3AacqXsURVlAmvEoPhML+ai9mNJRzmcu8zUNLSbGs3mOJdIMT2RnJb4vHU3wkUd/WPf59a6Afxf4ooj8DSDAAHBH3aMoygLT6FVpLR745mt8dvfr5D2PqG3het68HrWbvaJv1FzOBy+NjOMyMpEjM0sb74+PjfLQs4f44cDorPrVJcDGmNeBrSLSEbwfn9UoinIOUI9v8e59g3x29+t4xhC2LVwDw+MOyzuY86N2M+zMzZjLYrqRzBbH9UhM5BjP1h9EAfDqyTEe+u5hXjiSKLb93JquuvtLLXuFiLzfGPMFEfmDSseNMX85i7kuarZs2WJeeOGFVk9DaRGlG0il4lFu2ti+cy/fPzxCyJJiCSHPGGyB5R1Rvn3vza36CIuCwk2s1TeSevE8Q2IOG2wHBsd56LuHee7gcLFt86pO7rpxA1su7OUN/Z1So3uRmVbAhYg3DTtWzmvq3UAaSKSIhizyrqFQwk0Esq53Tj1qN4pWmoZmgzGGsUyes6kcrle/8B46M8HDzx5mz/7JLbCL+zu464YNbN24bEpdv3qoKcDGmB0iYgNjxpi/mtWVFeUcYqbd/8LKbiiZxRhD3jOEsRAB1xhCljXjo/Z8M5lpJrSFIZXLMzyew3HrD6IYGEnxyHNHeGbfIAW53rC8jQ/euIGfv3jFrIW3wIxeEMYYF9g+p6sDImKLyA9E5F+D9xeJyPdE5ICIfKXgziYi0eD9geD4hpJr/FHQ/jMReWdJ+y1B2wER+XhJe8UxFKUatXb/S4MTVnVF8Yy/Ew2+EFsifHjbG2qK4UIEOGgmtPmRzbucHE1zajRTt/ieHE1z/xP7uOvz3+fpQHzX9sb5L++6jL+7Yws3beqbs/hC/W5o3xWRvxGRnxeRqws/dfb9KPBqyfv7gb8yxlwMJIAPBe0fAhJB+18F5yEilwPvBa4AbgH+v0DUbeCzwK3A5cD24NxaYyhKRWpFvJWaJ7riEdb2xgnbFojwlg3L2PH+a/jI2y+pef35hk9rJrS543qGoWSW44k06Vx93g2DYxn+8qnXuOMfvs+Tr5zGM3BBd4x7b7mUhz74Fn7xsn5sa+7CW6BeN7Q3B7/vK2kzQM0dBxFZC7wb+K/AH4h/q7gZeF9wysPAnwKfA24LXoOfd+JvgvNvAx41xmSBQyJyALg2OO+AMeZgMNajwG0i8mqNMZQlRr2P7bV2///46z+ZYp7ojIXpiIYYTTt8+e6tdY1ZzcSx//QY23funXF+r50eI+N45FyPiG2xoiNKZyx0TvrYNou5ZCobHs/ypecH+NcfncBx/T79nVHev/VCbrliJSF7YYsI1euG9rY5Xv+vgf+TyU285cBZY0zB1+MYsCZ4vQbfvxhjTF5ERoPz1wB7S65Z2megrP26GcaYgojcDdwNsH79+jl8PGUhmauNs1q/2YbGVttAmo1/a7UxOyK+Z0XpNc6MZ0lm3WlmhfL57d43yHjWDbwthLxrODGaZnk+wkUrOmb8fpYi49k8iYn67bxnUzm+/PwAX3/5BLm832dZe4Tfum497/65C4iEGlO9reZVReQ6EXlZRMZF5DkRuazeC4vILwODxpgX5z3LBmGM2WmM2WKM2dLX19fq6Sxp5mrjrNVvoR7bZ5OQp9qYIjLtGomUw7L28Izz27HnIMvawwh+uSIJ/moTKeec8LFtJoVMZYNj9dl5x9IOD377IO978Ht87cVj5PIe3fEwv/sLG/nCh67lV69aM2vxnY1NeKYV8GeBjwF78Cti/DXwzpo9JrkReI+IvAuIAV3AZ/BzCoeCFepa4Hhw/nFgHXBMREJANzBc0l6gtE+l9uEaYyiLlLnmEajVb6FyLMwmOKHamKNph0/d9sYp1xhNOyxvj844v4FEiuXtUaIhm6FklpzrEbaEtmhoxo2/peI1kXc9RmYRSDGezfNPLx5j14vHmAjswp2xEL+5ZR2/etUa4hF7hitMxxKhKx6mK1Z3qc0ZBdgyxjwVvP6aiPxRzbNLMMb8EUENORHZBnzMGPNbIvI14HbgUeBO4OtBl8eC988Fx58JqnA8BnxJRP4SWA1sAp7H34jeFKTKPI6/Ufe+oM+3qoyhLFLmKpa1+i1kaGy9/q3lYyYzDqdGMxh8AS8Vwe0799Y1v3W9bRweHmcsnS/agLviITYsr25+OBczk83lhuF5hrNph9G0U1cgRTrn8s8/OM5XXhggmfHFuj1i82vXrOX2a9bSEa1fPAuELIvueJjOWAhrlhtzM62te0Tk3xd+KryfC/fib8gdwLfX/n3Q/vfA8qD9D4CPAxhjXgG+CvwUeAL4sDHGDVa3/xF4Et/L4qvBubXGUBYpc00CU6tfsxKYl1I65lg6x7FEmrxnWNUVnWZWqXd+129cxmAyR871sARyrsdgMsf1G5dVnUc95pfFlLt3LiaosYzDsUSas6ncjOKbdVy+9sIAv/Xg93jwO4dIZvLEwhbvu3YdX/wP1/HBGzbMWnzDtkVfZ5R1y+J0t4VnLb4wcyjyQzX6GmPMb896xEWKhiK3lnpDgWfbbz6hsfXmhig/B3wBfOloAhFY2RkrVjlO5fL0d8aK3hP1zG/7zr0cOjNOMjO5Au6MhbhoRUdFLwyAt97/DD3x8BR7ZMEr4Nv33jzn77tRVHoaKP+uCqRzLsMT2eJmWS1yeY9v/PgkX/zeUYYn/Iy6kZDFbVeu5r3XrqO3bfYhArGwTU9beMpcKzD/UGRjzF2zmZiizJW5JoGZqd9sQ2MLgvja6THGsy7L2sMsb49WfIQvFTFb4AcDCT70yAtc0t/BvbdsLrqvlYpguVmlnvkNJFKs6IjS1xkrthljappnZjK/LLbcvfWYoHJ5j0Qqx0Qddt686/HEK6f5wt4jDCazAIRt4ZfftJr3XbuO5R3RGa4wnfZoiO54mFh49vbhatS15haRlcD/A6w2xtwaBDxcb4zRR3tlwagkRvWsQhcq/0CpoGYcD88YhscdoiGbzlh4mkAVRMx3C8tg4QvxoTMTVV3Pys0qtT5fafjzmfHslJX0TOaZmTKTLaYk8FD7hjGbihSuZ/jmq6d55LkjnBzNAGBbwq1vXMX7r1tPf1esZv9yRISOQHgb4YpWr9Hj88BDwH8J3r8GfAW1rSoLRLVH+WZuJJWuCnOuhy2+29dQMktnLDxNoAoidmh0AgvBsvzzXc+UuJ55VUWw2kbZ7cfO8o0fn2T/0Li/SSOCkzccHUmxoiNMVzwyoy17pieDxZa7t9INI5f3eP916zmWSM2YMMczhm/tG+KR5w4zkEgDYAn80uUr+cDWC1ndE5/VfCwROmO+8C508EUp9QrwCmPMVwteEEGgxMJUpVOWPNWEqC1sNfUxuXRVGLGDjGeWv+kF0wWqIGIFsQYwxu9bzfWsVAQrmQHOjGf47O7X/UxrBhwD4Ke7dA0Mjfuua3/y7svrMs9UO6fWCrkV7mvlN4wLuuP8+jVruWx1V03xNcbw7QNnePjZIxw6MwH4xte3be7njusvZP2y2d1QbEvoioXpiocXJNR4JuoV4AkRWY4ffoyIbAVml/pdUapQzR55aDjFpv6prlaNfEwuXRWu6IhyYjQNHoQtqeihUBAx2xI8zyD4AtzXGS2KdS0RrGQGGE05uJ6f6rIgO4EWEwtbOK5Hb3t03oJYbYUMzX3qKJ/T9RcvZ2QiN2POBmMMew+O8NCzhzkwOFkf4uc3reCDN2zgohXtNXpPJ2xbRR/e+STXmS31CvAf4PvpvkFEvgv04fvZKsq8qWaPBGa0oS4kpavCzliI5fkIiZRDWzREf2ds2kqwIGKffvxV9g+NExZhVXcU25K63N0qmQGyrkcs5H/2CTcQIfGF3RiI2taC3YAq3Ry279zbks25vOsxksoxnqm9wWaM4cUjCR569jCvnkwW27duXMZdN2xg08rZpS6PhCx62iJz8v9dCOrNBfGSiPwCcCn+DflnxhinoTNTlgzV7JEbV7QzkXObWuKmPWJzMHiUvWh5G5/+92+a4s72x1//yZTH8sJPqTtZJbGuRCUzQMjyXcyiIZvUSIrAAgH4AtzdHm6onbbZm3OzCaR4eeAs//Ddw/z4+OTD95YLe7nrxg1cdkH9ZYAA4hGbnnhkThFvC0ltR7bqwRaXiAjGmP/RgDkp5xjztRlWs0f+ybs3A82plVZqh97U30HacUk53rRj1R7L5+KJUckMcNuVq9n10nFCtrCiI8zQuIMBorawrCNM2LYbegNq5ubcWMYhMTFzRYpXTozy0HcP89LRs8W2K9d2c9eNG3jT2p5ZjdkRDdHdFiYaaq3wFtBAjAANxJgbC+XQ3+paYrUCAYC6gwQWgtLvoj1iIyKMZ/NN+V6aEaBRb0WK104neei7h/neoZFi2+UXdPHbN27gqvU9ddtqpcSjIdxAj4byYes5SQMxlHmxUA79ra4lVuvR20BTH8tLvSQKTxX33rK5Kd9PIys0Z/NuXRtsB4fG+fyzR/jOgcm6a5es7OCuGzdw7Yb6664126NhLtRteRaRd+NXpSh6Mhtj7qveQ1kKLDaH/rky06N3pWPtEbuuZOozUW7CuX7jMna9dLxliXQW+mZY7wbb0eEUDz93mN0/Gyp6gGzsa+euGzZwwxuW1y28rfJomAv1RsL9LdAGvA14EN8D4vkGzks5R1hsDv1zZabIsfJjo2kHARzPzEskK9mXP7v7deJhC8c1U3I/tCpMeK54XlCRYoYNtuOJNI/sPcLTr/qlfwDWL2vjgzdcyE2X9GHVKaIFj4aC2eZcoN4V8A3GmDeJyI+MMf+3iPw34PFGTkw5N5hJuBaaRgUJzPToXX4sYlucTec4NZqZkiJytiJZyYSTy3vk8v41CxUwhidy5N2xeX/OZjGWcTg74ZD3qtt5T41l+MJzR3jilVNF4V3dE+PO6zdw8+b6a64tFo+GuVCvAKeD3ykRWQ2MABc0ZkrKuUQjbYblNDrHba1H7/JjW/7sKUZTDpYl2JaQ9wxnkjkcN1mxfzUGEils8e2eBSEH3/OskN5QxF9N5tz66pq1kno22IaSWb70vaP8rx+fJO9N1l274/oLecfl9ddd64iG6Frg5DjNpl4B/lcR6QH+X6BQYujBxkxJOddo1gbaXDf8GrFqzuU9EIqPxyLgiakrRWIpAhwZmcxdkDVe0f7pmSAizgCGOSeDmc/nr7dvPRtsIxM5vvz8UR57ebLg5fKOCO+/bj23vrG+umst8mhoGDP5Ab8FGDDGfCp43wH8GNiHXzpeUZrGXDb8ZrtqrldwwraQdvyVaVEkgYgtdV9r975BTo6mi+89w5TqvYKf2CdiW3S1h2tWwFiozz/bvvVssI2mHb7y/QH+5QfHyQQ3qN62MNuvXc+/e9MFROtYwZ4LHg1zYaYV8A7g7QAichPwaeA/4Zep34mGIytNpNqGXy1vhPJVc941DCYz3POFF7l6fe+09I/1itUlK7sqJEkPF6sU13OtHXsO4nqTuR7KyXuGNT0xQrY1Z7v6fNwEa/W96ZK+GTfYxjN5vvbiALtePF6sWtIVC/Het6zjtqvWFMPNazGfcj/nAjOt4W1jTMEL+jeBncaYfzLG/AlwcWOnpihTqVTCZzTtMDyRq1rKZiCRKv4jIeOIAAAgAElEQVShj6UdToym8TyD63nTzp1NFeV7btpIJGSzqjvGpSs7WdUdIxKajFL79OOvMpjMcHQkxaEzE+RdM+1ar532N9UqyVfUFkK2cGosS39nbM6BEKWfv0C9boLV+h4dmeBYIk2iSimgVC7PP+49wvse/B7/uPeof5OM2tx1wwa++B+u473Xrp9RfBei3M+5wEwrYLukuvAvAnfPoq+izItKj/D3veeKad4IOderusIrXTWfGc9iISB+Upvyc2dj4qi1+bh73yD7h8axRYpeDCdG06zujk25luMaLAs8b7oIX9ATpyMaYjTtzCvabj5uguV9Pc+QzDr0dcQqejdkHJd/+eEJvvL9AUbTfqqYeNjm165Zw69fs5bOWHhan3LqLPdz3jDTp/wy8L9F5Ay+J8S3AUTkYjQdpdJAKj3Cf2zXy/R1RElm80VBLpT9KaVUNEvd5HKu58eHGqGvMzrt3NmKVbXNx/uf2IfngYtBAmk1wEAizaUl2boiIYt0TrBsiptS4G/GFSpwzNefej5ugoW+E1mHsG2RyrnkPcN737Juynm5vMf//NEJvvS9oyRSvvBGQxa/etUafnPLOrrbZhbetkiInrZz26NhLtTMBQHF3L8XAP9mjJkI2i4BOowxLzV+is1Bc0G0jkor3R17Dk4Rw7G0w/GzaUKWcHGQLMdxDe0Re8oKGKoXvnzpaAIBVnXHiquxVC5PxPYd+MvrwJXmQQDq2pzbvW+QDz3yAhhDJa+xFR0R/uL2K9m2uZ/tO/dyeHickfEcOddMWQXHQn7wxQe2XshzB0fm5cEx1zwbedfjf/3oJA999zCnxtKs6orz3res49qgGrPjejz+k1N8Ye8Rzoz7BS/DtvDvrlzN+65dz7L22gUvRYT2qE13fPEkx1lA6rKZzCjASwUV4NZQLflLKpdnVVesGNF0cGgcx/VdtDav8lMPpnJ5P1m649WVPKbSWGNpP9tYd9wvOXRmPEsi5dAZC7Gpv3NakvKZxti+cy8/GEhgPL+SRulfVzRksbY3Xrw57N43yMd2vczZlAMYSj3Ywpb/OB4L23QFc2tW5eJCBNto2pnilVHA9Qz/9tPT/ONzRzg15tddC1nCu37uAn7ruvXFp4tqNKvcT4uZfzIeRWk01Xbac3lvSjL2gvkgUvIHW17255UTo0zkXDzP8HtffIll7WEQKeZXeO7gCBNZB8c1REIWm/o7CVuC45niOH2dMdqDBOyFFXQ9ScoLq8y9B4crbqoBYAx512P/6bGi10Yq62IJOK7vSxy2LAQI2f68nEyeVd3xGcddKB/nWhFsrmf41s8GeeS5Ixwrqbv2zitW8YGtF7Kqu3bBS9uSwKPh/HIlmw8qwEpLqbbxFbGluBKOh20/2sw1U1ZXpWV/fnTsLM8fHsESsCxIOS6psy59HWFePTnKcweHscVfVXbGQkWPhZlsyLXmWDinsLLO5WcqkygcS6QRoei1ceJsuhiGK4biKj/n+pFwUibn8bBdFPByk8l8IgNTuTwjE7mKgSSeMex57QwPP3uYIyOp4JPAL17Wz53Xb2BNb+2Cl+dScpxmowKsNI1Kq7VqG1+bVnYVbcHHEik2LGtjeCKHbQnGmGmbSQ9+5xCW+H6j2bxb9K0dGneKz4KeoZhXYXl7hB17Ds648bZ73yBjaYeTo2liIZu+ziidsTBpx6UjGmL7zr2+bTkIxqjm0xuxfe8L10DE8lfTY2mH0if88n5eUF25lDPjWZJZl8Fkhozj4RnD8LhDNGQXN+5mk48il/cYmciRyk0PpDDG8Ozrwzz07GEODk0U23/hkj7uvOFCNiyvXXet1eV+zgX0m1GawgPffI3P7n4d1zNEQxZ51/NLsF+9hl0vHa+4S1/uZVBrM2ki51KIZC03WxomDXKWJeBBMpPnWCLFp257Y83qwJ947BXaIkGZdNfjeCLNik6XfLBplnM9XM/DEqmZq8E/Zoqv950a84tvUlmw7RL3tNK5JVIOy9p9N61CNWaDn1+hMxau28fX9QyJVI5kJj/Nl9cYw/OHR/j8d4/ws9OTuS1ufMNyPnjDBt7QXzsi71xOjtNsVICVhrN73yCf3f06njGELJmyCn3u4Mg0395qdsxaOSfaA5G0hCmhwTApvoWnXxHI5r2i+aKaP++tf72HwbEMrjFY+Lv2eWOYyLqs7o4VbcfRkB0klZnFhrahaHqwgPIH/2jIZkVHhIms79FRmNto2i9LD749PO8axPJvBDCzj68xhrF0nkQqV3GD7aWjCR767mFeOTGZee3ai/yCl5euql3wcrGV+zkXUAFWGs6OPQfJex5h20IQXwhLVqELkcznP7z1Ij7zzAHynoct4ATaErb9kvGu8XfqDQbH8/CMH4m2fede7rlp47Rgh937BnltcBxbCMwevmCu6YnhGRjPuUW7cHvEZihww6qXvDFY4l/T4LudFQpwhmxhY18HqVx+iilmIJEil/cYnsiyoiPGio4oJ0bT4OF7g+TyNX18J7K+nbdSprIfHxvloWcP8cOBSff+q9b3cNcNG3jjmu6qn+N8S47TbFSAlYYzkEgRtS1cU3kVOhcq2ZM/evPFPPidQ0zkXNrCFsvaw2Qcl2TWpTtik3FcsnkP10BPPMQF3fGqG1cF7wzjUbxpeBhOj2W5an0v4G+k5V1DIu0UU1LWQ2GD0bIELxDilV1Rjp/1XbpWdUSLYnr9xmVTAlLyrsdg0hf75e1RlucjJFIObYHnRqWnh4zjZyrLONM3CfedGuOh7x7m+4cTxbafW9PFXTdexJvXVS94eb4mx2k2DRNgEYkBe4BoMM4uY8wnReQi4FFgOX5qyw8YY3IiEgUeAa4BhoHfNMYcDq71R8CHABf4iDHmyaD9FuAzgA08aIz5dNBecYxGfValNut623A9j+FxBw8/e5hrDLYlc0owUy3Rze1Xr+GK1d3TXLJKbcejaYe2iE1fUGyzWnKagUSKzqjNmXEH4/ruVgIgFOf8sV0vMzKRo07d9W8+hiDPhJ/r4MTZNI5nODWWZVVXlI5oiImcWxTTcje9wrwnsi4hy+GiFR18uorJplamsgOD43z+2cM8+/pwse3SVZ389o0b2HJhb1VvhfM9OU6zaeQKOAvcbIwZF5Ew8B0ReRz4A+CvjDGPBqWOPgR8LvidMMZcLCLvBe4HflNELgfei1+PbjXwzSASD+CzwC8Bx4Dvi8hjxpifBn0rjaG0gEJI6/IOGE05ZF2PkGXx4W1vmJPpoZLv8Jlxv5TP2t54xexjhXHeev8zdeV76IjYnBrNYFuC65miyK7tihZFXaBu8QXfzUzE3wCLhSwOD/tjRixY1h7Gsiw+futlU76TSm5yKzqijKYdvn3vzRXHMcZwNlU5U9nh4Qk+/+xh9rw2WfDy4r4OPnjjhVy/sXrdtUjIF96OqLqSLSQNE2Dj/8uPB2/DwY8BbgbeF7Q/DPwpvjjeFrwG2AX8jfj/0rcBjxpjssAhETkAXBucd8AYcxBARB4FbhORV2uMobSAKRtd1vyrZlTyyx1NObglARXVVrb15nsoiIxt+VnJjPGFszPuh9fu2HOQrniY4WAFXFjdFuTOEn+sM+NZ0jkXsYTetjAr2iNM5FwGEmk/4MICsSyGxx2Wd/hZ1HbsOcj+wSS5vEcql+dMMjslfLrWRlsy45CoEEgxMJLikeeO8My+weIcNyxv44M3bOCtm1ZUrbu21JLjNJuGfqsiYuObAC7GX62+DpwNsquBv3JdE7xeAwwAGGPyIjKKb0JYA+wtuWxpn4Gy9uuCPtXGKJ/f3QQZ3tavXz+3D6nUxXw22srtvR2Bx0OpKGRdj1ho5rSL9SanSWbzrOmJcWY8V8z3u6orynjW/29VuAlIweWiwkq4MxbyI9pcw+1XrynmdBhLO1j4q8qC0HsYhpNZBpNZ+jMOoykHxL9sLu9xLJFmTY+pmhs4nXMZnshOC6Q4OZrmkeeO8NRPJwteru2Nc+f1G9h2aV9V+2171N9YW2rJcZpNQwXYGOMCbw7KGf0zsLmR480WY8xO/MTybNmyRZNitJhSoe0IKtsOJjPTor0K+RuAooiGLD95TSmVVoozpZEsjD+WdmiP2mzsm/R5LSTu2b5zL0PJLGeSWcKWTEu80xG1sUQYTTusrVBm/uRoGoNvow2HbPKuh2t8M4cAiQm/1pwlvgcHIX+FfWosOy2JfLVAisGxDF/83lG+8ZNTuIHyXtAd4wNbL+SXLl9ZUXhFxHcli4fnXP5ImR1Nea4wxpwVkW8B1wM9JTmG1wLHg9OOA+uAYyISArrxN+MK7QVK+1RqH64xhrJIKd1YswUOBJFXduApUBrtBRQzmBVE9LYrV1cN6Cin0mq8PFAkbMsUb4PSxD0512NV4LVQ7vlgW5B2PD5688V85O3+VkV5LolYyPfIcH0VJl9268/kvSByTooblpf2dzKadoobc//lX37Mqq44v37N2mJ2MoDh8Sxfen6Af/3RZN21vo4o79+6nlveuKqiq5glUgwVPo+T4yxKGukF0Qc4gfjG8TfL7ge+hV/K6FHgTuDrQZfHgvfPBcefMcYYEXkM+JKI/CX+Jtwm4Hn8xcKmwOPhOP5G3fuCPtXGUBYppRtrB4Nk5oi/wosGPrKFaK+86z+S93Xmp3g7vGltz5zSLj7wzdf4q2/uL0bMZRyXVM5//B9M5pjI+u5bqVyekG3RHgnRFY8AwkAihWcCLwmRYhrJ5w6O8JHg+uU2677OKMcTaQxmmvgW3uZcg+W5RbPBT0+MEQ1bfOxrP6Qj5ttkB5MZPvPMfj7KJi5Z1cGj3x/gX354omiGWNYe4X3XrueX31S54KV6NLSeRq6ALwAeDuzAFvBVY8y/ishPgUdF5M+AHwB/H5z/98A/BptsI/iCijHmFRH5KvBTIA98ODBtICL/EXgS3w3tH4wxrwTXurfKGMoipVSkCiG2hRA2YyhGeyUzDsfPZghVqbU222q/+weTDE/kiuJroGhOEAHjGSZyLl0xG9eDvOdxdCTFio4Iq7rj2KOCZQxrevwNN3+OefafnowkK9/464yFWdHpMpF1GcvkESiuTB23tCry5Hw9/JtRLu8RDdtEbIt42GYim+evvvkaoxmHjOMLb3c8zPZr1/GeK1dXtOGGbYueNvVoWAxoPuAAzQfcWrbv3FsUqYND4+TdQmYbUwzTDVviJ9TxDGt64nTFJ5Oql/rN+j68IYwxjOfcaWkaS80dJ8+mSTnVS8lbJTcBmFyhCrB+WRunkxlc18PDt9cWjodt4e8+sKVoW66WT/ieL7yIG0TmlY9ByVhWkMgnbEHItljdHeds2glCiv3z4mHbD9YwHmu626YkTwc/R0N3XD0amoQmZJ8NKsDzY7Z5acvPL92oyrseAyP+RpVl+TZhzxh62iIkM3m6YjapnEc27xXzGdgi9LT5kVl51ytGlZVWFS4kMi8V+0JSnEr+vBKM7xmDMb7duRDGa4BY2KIjGiIxkaNwvygcswUuWdnJ479/05TPW24e+fn7nym6pBX6ViIkkDf+78LtophLQuBtl/bzkxOjRGyLWNgi43jkPcNHb97EzZf106UeDc1GE7IrzaFWCXbw7buvnR4rJkLv64gyNJ6lOx4unr/rpeNcs76bp/cNMZ7N++IrIBgs8W2Vf377ldz/xD72D44XAxrAFy3PGIbGc9NyMhwZSXPhsrZiReLy4psR2yJrvIqJaVZ0REjlXCZyfnpL33NgUoRd19DXES2W4ylcIWQJlsDBM5MpHKuZR9ojdnCDqZ3Kp2ArLrUZC9AWtfnPb7+Ex14+WTRLgL/azeY9/vmHx3nvdepiuVhRAVbmTbWqFp9+/FVSjkcu79s6wfdXHUs7eAbaIyEk4vcbSmb4xk9Os7Y3Tt71cAJxXd3tmxqGkhk+8ugPSOdcXM/UFKtyBhIplreHOZZI89b7n2Es7eB6His6YkE4cAZbfFNHQYctoC3iJ4KfyLkY/M05pJAHwQ8ZTqSmR7i7ngHLX5XPxHjOZW1vnFNjWbIVkqFXQvAF9qLl7Xxg64Vcu3EZO759kK5YyJ+fCLYlRGyLE2fTdX9PSvNRAVbmTbWKEfsHx1nbG2d4PI+FFB/nc3kP2/L72aNSLC3vuIajIynyrp+IXBDOjGcBgogzE6xEoU6tAnxRHRp3iIWsygltOsKcGc/huYZY2CJqCWNZlyMjacKWnzynWDTTgMGQzOZZ3u6bRKaNhz+/i/um+iDXSkhv17kX1hUL8eCdW1jRMbXu2uruOIlUjo6oXdxYq7eq8kKXNVLqRwV4CdGoP7Rq4b3gC3HRq4HJbGgFAY2IkM65RbtmwayQcw1hy/9dEOFCpFveM7ieV3RbmGk1XDjeHQ9z6MwEOdfDwjCazhOyLDYs76A7lsXxDK5n/HpnwbUdD2wx9Hf4IcQ51wvKI3ms6IgyPJEr1pUrUNDSj996WbGtmpnmV668gF0vHZ9x9Svi54xoi9hTxLfg0fCRmy/mk//zp6Qdd1bl56smNjp2dt7VmJWZUa/rJULhD61Qi6zwh7Z73+C8r33PTRuL9duMMcVUihtXtJN2XCK2NbnDX5aS0vW8KcnIS6N6Hc/4Ntq8XxqirzNKX2cUY/xVcJUI4KoMJrNMlJgwMo5Le8RmIJHi0HCKvOtxajQIrii5sGtgLJNnY18Hm1d1cXFfB7ZlFT+bJf4qvuA5Fw1ZbF7VWTG9ZVvEd/2Kh20Ew579w3zk5k1EZ4g8M8Yv3JnM5Hn+4AixsM2q7hjrlrXRGQvztstWct97rqC/M8Zo2qG/M1ZX9eTyebVFQjiuy2d3v96Q/yvKVHQFvESoZqedTf2walQL7wW/nHtnLOSbEEqEzbYgVKGMT7mgpp3JYIQjw6lZCW45hslqGYXNrMMjKS7u6+BMMluMbCuknSwdrHSFmnZcNq5oZyLn0hkLMTSepTTHuWsMt75x1ZSxC2YaY/xVtmv8DcmToykyrktnPEzKydacvxWsgP9m9wFWdcem/bvNJd/GfBIbKfNHBfgcZbbmhJkq+86Xan/8tx87y4PfOVQMiy24dnVGbXraIhwZrj6+7/8qRV/gSuJrie/yFSySZ6Tc2SGX9xhKZsm63lQ/3JLNuMLYpcVA/+TdflqT+5/Yx6mxSeEUfO+If9x7hDet7Sl+J2t74pway/gRaca/ViLtMJF1+dPHflpxruWlilZ3x+luiyyoGFYyH9Wb2EiZP2qCOAeZizlhXW9b0S5bYKb6YfOlkF9hPIj2ssV30eqOhRjL5DlaQ3yhZMVa5fhkoU2LWNgmNocEMp6B08lsxUHCFoRDVtEjovzRftvmfnraIv7GXeACFgvbhEMWo2mHjzz6A956/zP8xt8+xxWru8jmPVLZPBO5PEdGUpwZzxX/TbZuXMaO91/NxuVtREJCyIJo2Cp+b5bAybEMr50a41gizfOHR9i+c++8zQKVzEf1JjZS5o+ugM9B5mJOqDcN40JRWoiT4LG/kDc3m/f84pyFx/0yW67gn1coI19NgP1VKUWvCkukapXhmSiNcJu0QQOev+n2uzdtLCbXKWUgkcL1zBSXM2MMOdfgenlWdcU4OZpmIJHizWu72bP/DBO5yRvhlgt7uevGDVx2QRcAd9/0Bu5/cl9gRw/8jY2fNxhjyHr+bKO2VC2nNBsqmY9mk9joXKfVHiAqwOcgA4kUtsDBofFirtoVHZGaj4i10jA2gtJCnPkSO69nDDnXwwQliTav6mIs7XB0JFXMx7B+WRunxjJk815dYioUvCrmH9VZScBdz/Dfv3UAYJoIF5KuG29yc7Fg1y5NgDMykePxV04X31+5tpu7btxAJufxd3sOcXIszeruONdvXIbreeTyZTce49fjKs4z2DAr+FvPR0RKzUcFQUrl8uSCrGyFwqDnm/23VgBRsz6rCvA5SEfE5sDQhO9wL36Z9+NnM1zc116z30JUH66X8kKcBfuqZyBuW767mcBY2im6mQm+zy1QsXJvNQpnRkv8dW2ZXCHPRparjeq4hs/ufn2KXRf8J4uP7XqZsykHr2TOlvhJzY+fTU9Z8V5+QRe/feMGnLzHA08f4PDwBGFb6O+MMpZx+LvvHMLzTDENZ2ED0g0+h5+4R3CDLzTvehweTrPBM/TEwxweHueeL7xIR9TmklkKZ6kgreqKTVn5nm/iC43dmK4XtQGfgxQzWEnJT2n7ImBdbxvdbWGMKUloExzriofojIUIW8Lxs2kc18OSQgCD4dToZG6EeogG+Q88KKZV9ILcDRHbKl4rZEnp11UXIqW+yx479hyccnzb5n7+/NfeRH9HBMcLzBYBZ8ZzRfG1Ldi4vJ3/vv3N5F3DA986wPHgSQYjnBrLkncNjusnd3c9M2XD0ODfYHx3N/83+PbrsGXRFgkxns37hU+NIeN4s3Yfq+SSVgjhPh8ZSKSKodsFmr3ZqAJ8DlIolxMKCkaGLGFNT6xYLqeR7N43yPade3nr/c9M2wQqPZaY8AVleUe4KIIifiTXhuUd/PntV7K2t42QJUFiG5uVnVHCtkU2EKFCcvaZKKywbZFiEUqD70ccCi4Qtf3k5vWUUA+VnxMk2ona1rQ/zmTGYWg8ixEJqhr7f9ClyX0sAc+DX7hkBSLC1148Rjy4Ydi25Ve/CKL+powsU28WHr6HQjbvkXc9zoxncFzDyi4/MGMomfU/owg515u1gC4GQWomrdiYLkdNEOcgBdeh8nI5/UHJ8kZReER1XJfRlMPJ0TQvHU3w4W1v4E1re6bY09KOnz+hJx4hZFms7W1jVVeEp/cN8fzhEV45MQrAyq5ose7aeDbPyq4oR0fSWBZF17WZyJT46ErJbws/KCKVc8m5hpA1c9IbmIzGg0nTieDfJAp/nKU12B767mFG0w6pnDvtWrb4tuCIbfG1l47xb6+e5sx4jlVdUSK25W9EBqvsnOsRsvxVdKkrXAEniA60LN+EMzLhsLo7VqxiUYjSMx7FFfJsBLTegqXnC83emK6ECvA5SKv+4+zYcxDHdRkcyxYT13iexwPP7Ofivo5p9jSAnrYIj//+TTzwzdf4zDMHsMTf0S/MeTybJ2xZU2zZALVMwLU8HUzJb8fzfW3jYYuU4xXNA7ZALW1fvyzOkZGpSWwMMJJy+NnJUd7+33b7uSDaorRHbV49lSyeF7b9p5LCInpjXwfpnMvJs2kQYcPyCMPjOY6fzdAetkgFG42C/710t0WK2dXKCVuw+YLu4vtULk/YElKOV3xdCIle0eHfjGcjoItBkJpJszemK6ECfA7Sqv84A4kUw8nsFPEqJJ45MDTO5lVdU84vXX09+J1Dgfj6KzNLwHGDKLdSw2wdi97ZbKoZ49doK8U11UU4GrLoikdoj/g+uuWbeCPpPGfTeWIRi9MlARi2Jaxoj9AVC3EssGsXbNBHkynyBsQYDp2ZoDseZiiZZTTrTvnYhWlWu8FY1lSLYTzs+yZ/6rY3smPPQUbTDslMnt42v8xQISS8XgFdDILUbJq5MV0JFeBzlIX6jzMbP8h1vW1+ohomN6YKSuEappWKTwe5Frbv3OuX3hHAeNMKP4YsmVL6vXz1OV8qiZlIEGlWZqtd1eWvHDN5b4r4lq4uPSCV89XSFn+VH7aFcLBB1hkLMZTMkRfDqyfHikIfsvxNxkTKKXpblM+tsPq1oOg/XfhdahqBydVtJTeyuQpoqwVpqaECvISZrR/kPTdt5LmDw/6bMuGyRHBcw1AyQzKTJ5v3EIF4yGI04/hdDDjG4BmXSMgurv7aIzbpCZcJ1yWdaE7+2nzJatMKNjM9A6dG06Ry+SnlhYAp2c4K9HVE6IqHmMi6/OE7L+Ur3z/GydE0y9oiZHIuOc+U2bH9UvOON91WXI5hMvubweDk/YDo/YNJP/DD8kvI/8m7L5/STwX03EK9IJYws3U72ra5n3W9cWAyTNi2BBHh4v4Obr96DYmUQybvErF9l6+JnG8zLvVmcA3kXF+EwrYwOD5Z16xSaaBGYvBXloXp5Vy/skataVgCsZDQ2xYh7xrWL2vn165Zx1d/93q+fe/N9LZH6euKFVfTBfKeCZLx1Dcvr3i+v1HXHg0V80gUPDOUcxtdAZ/HzGRemEuCnk/d9kb+cNfLJDN58p4fUtwZC3HvLZvZsecga3vjRTPEvlNjRRtvSILaasF1PA/+j7dv4m//98GiGLcSU/a7Gha+8K3oiJL3PFI5l7F0jrfe/0zxOy5EKp4cnQwwMUxuDNZzj4nYQsgWsnnfs6G/K0YkZE0x8RSqhHTFw5qz9xxFV8DnKfUk7JmLH+S2zf38+e1XctX6Xi7ojnPV+l7+/PYri7XWSv1II7ZVjEYrFx4ReNPaHjL51ovvTBRWmhFb2NjXzqb+DkSEaMguimrpd9wZDXF6zPfJDZfYuwtRbCKTwSnl2FbhR8jkPdoiNh/e9gYMTPlux9IOwxM5JnJ5zdl7DqMr4POUUvNCIdw3m/f4yKM/4IH3XsW2zf1zdjuqZmcs9yPt64wyUSXjmWf8VI5SGqe8SAlZvtnkb3/rGm6+fGWxffvOvcWAB5gMZTXG4Hh+FRArqM9WMHMYA53REOmci2CmeWJYIsRCFv1dseK/ya6Xjvt9SjY5S6uElOaF0Jy95xa6Aj5PKaxGx9IOJ0bT5F2DbcFELl9cKW3b3D+nKgrVKE9taFtCd7z6Pf7VU0k/SXuDmWuEdjHdpQiXruycIr5QPXJsIueyqa/D39wzhmjIYv2yNi7qa+fq9b1csbqbFZ0RYmG7WEHZDqIE37Cinf6u2DS7vDFmyndbWiWkdOzzNWrtfEVXwOcphdXomfFsSUFMiIWsKSXaF3rX3PM8DiYyGANtYZvf/YWN/OU39y/Y9efCXBfYhfJC/V0x7r1l87TjtSLHCk8XYVsqPl184rFXWNUdmnLsvvdcwR9//ScV7fKl/r7HEinaIjbtURuDvjgAACAASURBVJvOWHja2Mq5g66Az1MKq9Fs3gMx/gZYsGJqxEpp975B/nDXy5wayxK2JKgk7PHI3iM1+y1m44MHrOiIVH0qqFYLr7AZdvvVaxhKZnn1VJKhZJbbr15TvOFVe/KoZZfftrmfL9+9lW/fezMPvPcqwrZdcWzl3EFXwOcphaimjzz6AyZyeWIhi77OKJ2xcN3lymfDjj0HSWby2JZv9wQQzzCWdhZ0nGYjIlWfEGpFju3eN8iul47T1xllfYktt5DOstqTR712+aUYtXY+ImaRb4A0iy1btpgXXnih1dNYcEqDLcofd2v9sdYTIVd6zlAyi5P3iIQtJLCeFmyV5/r/sD94+6aK1TBqsX3nXg4PjzOWzhej/Lrifia4L9+9tWbf+UazKYuCunYedAV8njOXlVI9EXLl55xJ+gl68q5H2A4iuGaZDH2x8tdP75+WiH0m9g8mGU05WJbvBZH3DGeSORw3Oe3cSje7mURaOT9omACLyDrgEWAl/t/hTmPMZ0RkGfAVYANwGPgNY0xC/GzinwHeBaSADxpjXgqudSfwx8Gl/8wY83DQfg3weSAOfAP4qDHGVBtjoT9jq+tJ1ctsN9rqqRRQfs6q7hgDIylcDywJapk1O6ytQXiGWZeqyeU9CEK0IahxJ8ZvL2ExlMVRWkcjN+HywH82xlwObAU+LCKXAx8HnjbGbAKeDt4D3ApsCn7uBj4HEIjpJ4HrgGuBT4pIb9Dnc8DvlPS7JWivNsaCUdh0+sHRBKdG0/zgaII/3PVyyx3hayVMr5d6EnOXn9MZC7O8PQL44bxOkK+gJx6anuB8EdIZtWserxSiXeu7Dgex14Vw4oK7XcSe+l00sgrFQvxfUBpLwwTYGHOysII1xiSBV4E1wG3Aw8FpDwO/Ery+DXjE+OwFekTkAuCdwFPGmJFgFfsUcEtwrMsYs9f4huxHyq5VaYwF4/4n9pFIORggFER8JVIO9z+xb6GHqpu5lKuvRD0RcuXnJDMOIymHaNjijau72LiiHddAdzyMLHJDRMQW3rimp+Y58bDN/tNjRUG79a/38LFdL1f9ri9Z2cXy9gihoH5byBaWt0fYtHJqys5GVaFYqP8LSmNpihuaiGwArgK+B6w0xpwMDp3CN1GAL84DJd2OBW212o9VaKfGGAvGwTMTxSxgEmS5ssRvbxULtZqq5V5V7ZxTo34i9c5oiENnJjg87JdrPzKSxqkj+UwrybmGgeHxmuecGc+SzLpFQTt0ZoKzKYe8ayp+1/fctJFIyGZVd4xLV3ayqjtGJGQXv8PC6nQomeXA0PgUb5GF8OddavXdzlUaLsAi0gH8E/D7xpix0mPByrWhy6NaY4jI3SLygoi8MDQ01MhpNIWFWE2VliUfSmY5NZquGCFX8GUNW8L+wfEgf66fSWwi555z9t8To9maxxMph2Xt4aKgucavelEICYap33UtX9/S1emqrmhQCSTNWDq3YP68S62+27lKQ70gRCSML75fNMb8j6D5tIhcYIw5GZgRCs9Ex4F1Jd3XBm3HgW1l7buD9rUVzq81xhSMMTuBneC7oc3ms120vI0DQxNIkCrQGH+z5uIVrYtEWtfbxqEz4yQzk65PnbEQPfEw23funXGzcC5lyVOOx9reOMdGUmTrrOG2GJlpkd4ZC7G8fTLsN2JbOK5HriS3ZPnKtdrmZ/kGpohwajTDqbEsV6/vXZDN3KVW3+1cpWEr4MCr4e+BV40xf1ly6DHgzuD1ncDXS9rvEJ+twGhgRngSeIeI9Aabb+8AngyOjYnI1mCsO8quVWmMBePjt15GT1sYscA1BrGgpy3Mx2+9bKGHqpvrNy5jKChwaQVFHgeTWU6cTddlC5ztY2v5+ecrlsCm/s4pNu++ziie8bOWzTYSrdIG5sX9HfR3Rvny3VuniO9cN9KqmZGu37hMN+YWEY00QdwIfAC4WUR+GPy8C/g08Esish94e/AefDeyg8AB4O+A3wMwxowAnwK+H/zcF7QRnPNg0Od14PGgvdoYC8a2zf38xe1XctW6XlZ1xbhqXS9/EaRlbBXPHRyhvzNCxLbwjL9KCwVpDU+NZvjZ6SSnRjPk8m5FUZ3tY2vp+flzzOQwGzqioYqJhnrbwmxY1jbrREb1pgGdz0ZaJRPI7VevYddLx3VjbhHRMBOEMeY7VI8G+cUK5xvgw1Wu9Q/AP1RofwF4Y4X24UpjLDSLrfzLQCLF8vZosSIuwCsnRvEMCKZYeXh4IkfeHZvWf7aPrYXzXc+cczbferEtuGJ1dzGg5f4n9rF/0N+wu2h5Gx+/9bKq5pxqPuL1hhvX449di/L/n9t37p3X9ZSFRyPh5sFiC8SoZAMu6KLj+UUmRfyfXAV77T03beRju17m6PAEQbZDwrZw25WrK37WgpCcGk0vckez2sRCfhn3E6OZ4Gblmx1E/FVuqTBO5FzW9saLwlkpaGKm4Ip6oxPnUrGkFgt9PWX+qADPkcUYwXT9xmU8f3gkcI9jygZRQYhn8jvJOe4UtzHv/2/v3MPkKssE/3vPOXXp6upbOt1JyIVcCARlRBAVkMUs42JwfGDc4dkl6wXREUfdYZxZXXB0nFH32dVxnnVldZXsLKKO4ijjBS/IKpjBVQIEGG4mQEgCSUjSSafT3dVdt3POt39851Sf7q7qru6urqru/n556ulT3zlV560vdd56z/u9F1/x97/eTyJm094SG/9Zr34l1164uuHlJufCqo4EnS1xOlNxcq7PSN7F9RSWJWxc3srN27ZUzP6rZEFWc1w1d09ru1IV60nMBrMw13wYBTxL5np7OB+EPuDwgrVFKFbQtsUynSFve2A/BV+RsC2sIHvNV4pMwSPn6v5nUUWw0GNKBTiVKXB0MF+yemO2RWcqVmqzFKVaC7JWlma5H9S+4QLbX7tsRu8TMtsOKIb5w9QDniXNGGcZ+oA39qTZsrIde4oU4FzRn7QSfmhglKKnKHg+uaJH3vV0Gq3SLgs3SC/Ouz5HB/M8dKCfx16qeYmNuqFgXOicr3RES6WMxmoWz3bu7WMoW2TPsSH2n8gwnCuWPa4aHtx/ip50HAsouEp3NRH42VNHp31tOWrdAcUwd4wFPEua8XZuokyFKfqfK5jkOmlLOBwJmmeGsc1RX3FYSCZqUy+acqZB62LX059/z7Fhtu/YNaPFs9AtlYrrfQXP58hAluVtHjHbnrGleWhglIRj4aMjWkT0HcnzJzKlllIzpdkWjutFs63XhBgLeJZUk647n5SLD50UKjVFbG64yJSKOxRcjw9++zH2Hhsea89eppTkRPexr8ov5i1kwk8jMClMa6ouFzDmluppS3JGR0upK/RI3puVpbm2K8Xx4bGWUiL6EbOsBe/+qSfNXBfDKOBZ0sjbuUpfKGCcTBuWt1Z8j3BRbihb5EQmz2jBmzaSYbGkWoS/S9HPM/EHR8GkmOlol4tzV7bR05bgrseOjHPhhG6p9pZY4Apqo6MlNqvvRfiDGv4L20qtaE+YyIUZ0Mx1MYwLogbU2wacagEwmkm1c28fN9zxSEX5hrJFjg1mmcJTMY5FY+uqkscBC52GHD4PiVljMdMj+dNs37GLx14aQARWtCWR+ORW8LV2S23d0svZvWkOnBzB8xVxW7eVsi2hty05/RsYgJkvitbTXWEs4FnSyNuaahYAQ/mmKsV7+HT19RsWsvUbs4WEbZU+g0J3O+5siZWUrhWZKO1vFYq+T9FTDOc9DpzM4Pk+vq94eTBbql4Wnff5cEvdvG0Lve1J3dZ+eSu2JSZyYYZUm3kI9b+ujQKeJY24rRlXwrAvw/HBLPtPZNh7bIh9JzK0xseUcihfMmZPKogePq3W8oWFbf12tcRwbN2pTtCxvyvaE2TyuonomctaWN+dwhKwBRSKouePa2ffP1LAFsFXOhrhpVOj7D+R4WQmX7qQq3FLzbS2g4lcmDsz+WGs93VtXBCzpN5ZReMrlSU4dCpLX6aAY+maxOHt8q2/fI4H95/i4YOnSNhCOuFMioZYpFnDFcnkXVZ1tlD0FNdeuJoH95/isZcGcGxhRVuS9uD/Me5Y+L4Ot1PoHyql9F8LwVc+0Y5CBc/nRKbAf3jdskm3rZ+55ryyTUxnk7yzVCMXasVM+iLW+7o2CniW1DpLaTom+n1jTp686+P5kIhr32De9fjyzhdY09WCI5At+owWC6X2OCET/Z2Lnbyn6G1LcsnGZTy4/xSHgotpRVuipHzD54dPZxGBhC2gpFRZTgRcDxxLSnUvwv/z7+4+xJd3voDr+yRsC8/3yyrWuSbvTFTy0c/TTKFVzUi1P2L1Di81LohZcsnGZRwbzDNS0LGgIwWPY4N5Ltk4uyyl6Zjo9/WVIuEIji1s7EnTlowxOFrE8/UtshtZ1Q8D+IXwFntxEZvmW/y69ct4/+Ubx1UCE4Ejp3OlRAnQraU296RpjTt4Pji26EQIsfACf0SYLXdmd4qNPWnitsXh0zl8pYjZFp6C/kyRoje54txskndCl8Xv/fXPueHrj/DQgX4GRgrsOTrIF+/fx8H+TNOFVi1k6h1eahTwLPneo4fLxsl+79HD5Q6fMxMXEqIlJ0Pyno9tCYcGRsdVJ1OApwAJ/i4ypmt5dMnGZSXr0/UUB06OUHB9XF/x8unsuAvt3FVt+gfMV2SLHgpFdzqGJYJj6dZTZ3QmaUtqy/n4kO6IYUdaU4nA4GhxkmKd6WLQti/8M+/9xiPsPtjPcN4rFf3Puz6DWReUYijrNl1o1UKm3j5344KYJYcHsjManysTs7Dakg4nMgXaWxyU0srCEqHo+hW7O9Qjaa0jYTOY96Y/sI58c9eL+EqRTji8PJjDQiIdLRTHhnJs7m1jZXucu588pq1cSyv2E5kiazqT3PaO1wC6PX1YhD1b9Cj6PgnHKlWaA/037/klxRq6Dp7vG2Y459KVirE8nahYiyG6Em+LUAx+TENHkq+0j1onwoz9bzc6FX6xUE+fu1HAs6SSLpsvHTdxIaGzJUbMtugbznNqpMiG7hQr2hIcG8qXWqA3gmZTvqD7ucVti5HCWFaZ56vSYmTB9Xn/5Ru56TuP4/uK6CcQYCjnli7IiYs5MUs4nS3SP1IAXytfTykcy+L9l2+c1OYpZuc5mSlwMpPHtiw2dKd48vDpcb7dgZE8MVvLaIc54ejvljC2MDjxDqjRqfCGmWMU8AIi/GXeubePj9z1BJm8ix90Zzg5UkApRSomDOUXoZ9hDri+wvM9RMYW0cIU6pgFowVd13co5056rYJx4xOto1DBdrfGGc655F3tBvrQ1k1s3dI7qQh6wrFRSvubz+pJczKT54v376O3LU53a4K+4RwH+0dY06lTmXXX5fF3L9EM8+gdkIkPXngYBbwA+ew9exgYKeAHC22upyi6Be0jbLRwTUpoPRaDYjugFyQtsXBsmRQpUi3ThThNDGs6MZzHEvB83c5+OOdiCQxlXZank9qXa1kcH86zoi3Jy4PZUrZe+DkstAJ/y3krODZUmDa0ytC8GAW8AHnh5AhekE4rQUzZYlxcmw+i0+Qp8DwfV+muxFMxVfWxqXyG5SrUCWOug2gD1ZAV7QkOn87h2MIZHUmOD+fxij4tcZtU3GZzb5tRtosEo4AXIG6obUOjbakF9s6SSq5xC91qqBICpVjdmdYJmLh4aovg+orlad3iPm5bpTjyEMe2OLs3TWcqzuGBUS5YW5tW9YbmwyjgBYht6YvYKN3aUJxm0dK24PDAKDv39vHRu55gOOfi+j4nh/N89K4nynbPCJnootiwvJUTmTyOrSMpykWzFD3FX/3BFqNwlwAmDngBclZPK6HLMurPNMyOqEtgIlZwxJquFJ/7+V4GRnWTJyeo9Vupe0ZI1GJe05Xi5m1b+Ltrz4+UDE3zZ1ecxfrutKn1sAQxFvAC5Jarzi1FQUS7VIShSYaZEbMF15+8fJmMWXi+whLRXaD/4VGUUnohL+wwDew/OVL2fSvWfrj6ldx548Xjjr1pFnI3a5cHQ/UYC3gBsnVLL++6+EwErXDDVupG+ZZnisYggO7qUW7uip6PJWMhZZ7v4wXzHSZCeAq8Msob5reyVjN3eTBUj1HAC5CwM4MCnOB/cDFHQczVuzJVBuBU9ZJb4w4f2rqJm950NgBx2y7JEz6i4xOZz8atzdzlwVA9RgEvQMKLz/PVuPKIi4VocEesihjdifWOJ2IJ9KbjdLY42JZgW0J70uEv3rSZmG0FJT0nyzCx5VAqYWtfe0RAW/R4OWZS+2GmNGNXbsPMMT7gBcihgVHakw62QHH6w+eVWkfAdSQdlrclGMoW6W6Nc+R0lsw06c2WVPZ/d7Y4rAkU4VC2yOaeJJmCx9quFK9a0xm8XojZFnnXK/WGC3vC5VyPm77zOLdedwGbe9tmVIJ0ui7K1VLO19uMXbkNM8dYwAuMXNGjN53g6GCOYhOUXail8nUsIef69LYl+fy153PLVedSCFJxp6Lol/fhAmQLHoPZYqlDcdFX43ymvW2JwI+uJjXmHC14WIylKl+ycRkx22ZlR5JzVrSxsiM5Zbv5WlTWquTrvWTjsoZ25TbUBmMBLxCyBY/+kTw/e+ooL50aZWC00bZv7fi91R0ADGULHBvKc2hglNse2M/ASH7K6m6gLXAn6JNWjoKn+PXNV7B9xy4Knj+pGHrMsuhKxRjOuWV/TIo+JB3tBnlw/yk+ffUrq+qsEDLXylqVirhHZXn++BAFTxF3xtrVm2iIhYFRwE1OJu9yaiTPfXv6uOO3B3mxf/H5+PYeG8IWoeD6xGNWydI72D+CmsbHoRciKyvgcLRSq5nBbJHPX3s+tz2wn90HT5XaEUVfq5Qq+Vfr3R5oqhY5oRyfvPsZOmyhJWZX3ebI0BzMmwtCRG4XkT4ReToytkxEfiEizwd/u4JxEZFbRWSfiDwpIhdGXnN9cPzzInJ9ZPw1IvJU8JpbRfSNaqVzLCSUUgxmi7zUP8IPHz/MDV97hE/9+Hcl5dsat7GAuCUkHYtEkERgw6yLyjQSSyAXWLq5ol9qwx6zrKpqGFfIoQCgJWiXMdWC2NYtvdx548Ws6EiybllLaUEurAvs0zj/6nQLeSYaYmEznz7gO4BtE8ZuAe5TSm0G7gueA1wFbA4eNwJfAa1Mgb8GXg+8DvjriEL9CvC+yOu2TXOOpsf3FadHC7zYP8I9Tx3lfd98lE/88BleOKED/S/d1M3t119E0fNxbMEO2qeHLdXDyzThWCRsmTY6oFmY2J15tOBxeCBLe3L6GzRBVzTrSpU/9gNv3ARU12pmbVcKx7ZY25XS0ReWnl9bGtcKfjq5TTTEwmbeXBBKqQdEZP2E4WuArcH214GdwM3B+DeUUgrYJSKdIrIqOPYXSqlTACLyC2CbiOwE2pVSu4LxbwB/CNwzxTmaFs9XDGWLDOWK7D54iq/95iBPvzxU2v/a9V285w0buHTTctpbtKWjIqZhtP1Q0VMICkugqzVOwfXL1rltFqygRq87YRXN9XWX50rRDYLu2RazhLhjleJyo4cmHSlFOlTTGTeMWohFqpC5HmzqaeXmbY2pzTCd3CYaYmFTbx/wCqXU0WD7GLAi2F4NHIocdzgYm2r8cJnxqc4xCRG5EW1xs27dupl+ljnjej6D2SLDOZcnD5/ma789yOMvnS7tf/XaTt5z2QYu3dRNVyrOr587wW0P7C+1TveVhxO01olS6gGnFGd0JMnkM/OWJWdbky3YmWCJkHcnyw/6M6zqSBC3bY4NZsl7Y6154kEboI5UjPXdaQZG8pweLZTiouOORWcqNq7j8HT+24nKrlmqkE0ld61C3QyNoWGLcEopJSLzmr813TmUUjuAHQAXXXRR3XLJip7P6dEimbzL3qNDfO03B3j44EBp/yvPaOeGN6znsrN6WNYaJ+5Y4+oKnNGZ5PBAFtcH3/crrlGdHNGFY9Z2tXB8KE9ullkbpbrDAUqN+V3jjo1jaSXquj4zjYwrZ/2Gp1LoIjknM3l8wBFK3Z5tgY7WGEVPu22e68touSQolKOgf6SA6w0xGxZKYmE1lr2heam3Aj4uIquUUkcDF0OYuH4EWBs5bk0wdoQxd0I4vjMYX1Pm+KnO0XDyro5JzeRcXujL8LXfHuS3L/SX9p+zoo0b3rCeN5zVzfJ0kpb4mG/vtgf2U/Q8+jM6CSBmW9hKd1UoeH7FxarhnEtnKo5tCQnHmmRtVkMqbrOxJ81wrsjhgSyWpRVn3vPxfMWfXL6RV63p5L3f2F11QYrQXWBZgh24GaLK0wtaLR05nStlrykRfNcnGbfpbo3TGrfpHymUPr9CK2jxFY5t4UdaD1VDxeI5zDyioJ6FcuodmWGoHfVOxLgbCCMZrgd+FBl/VxANcTEwGLgR7gWuFJGuYPHtSuDeYN+QiFwcRD+8a8J7lTtHw8gVPY4N5jgykOWZI4N86se/433ffLSkfDf1tPKZa17Jjne+hre8ahVrl7WOU74Az/cNc3K4gOtrxRR4GGhLOpzTWz4TC3Qc7GC2SM71mK1dl3AsXugb5mD/KK6vcD2FpyBmW/hK8eWdLwCU/NITLeZyKCBhC6s6kixrjZNO2DiOhacUYkFnKkZPa2zs4OBhWcK6rhZ+ffMVdLUmaG+JkYrrFOPwlJ5S+IFGjjvVf8VrFVFgCuUYqmXeLGARuRNtvS4XkcPoaIbPAt8VkfcCLwL/Ljj8Z8BbgH3AKHADgFLqlIh8BngkOO7T4YIc8EF0pEULevHtnmC80jnqzmjB5fRokVzR48hAlq8/eJD79vSV1OCZ3Snefel63nh2D12pOJ2pGFJBcxVcH0T7TEErOF8UBdfnlqvO5d13PFL2dQA96QQF12d0iq4PlbAEhnJFQEpWqw9BV+EwA03xuZ/v1bIrVVX1nLhtsaozWVo8Wp5OlDpAhLfRn/jR06zuTHIyo61cK/jce49n2PCxn6JUoMQ7W0g4urOEG1Q2cyyhvTVWMU24HFPF3M6ESskTUX+0YXqWQrnN+YyC2F5h1++XOVYBH6rwPrcDt5cZ3w2cV2a8v9w56kkm73J6tEDB9Tk2mOObu17k3meOle7OV3e2cP2lZ/Kvz+mlMxWnKxXDmSqYFR3fmy3qULVol1ylFLc9sH9c48aJnMjkufW6C/jk3c/w0qnqlUloyTq2xebeNvafyJRa90T9tgI815chFizIlXOHOJaUFLYlcEZnkrakVnZhQsQ9H7583GvWPqBX+Df2pBnKFjk0MFqaw/AceU9xeCBLd2ucgdEijq2V+8qO5IwXo2oVUVArRb6UqaU7qJkxtSDmgb4h7W74wi+f4123P8w9T2vlu6I9wUfffA533PBarj5/NWd2t9LTlphW+QKcvaKd7tY4ji14SuHYQjphU/AUfcM51i5rKfs6W7QfGODTV79yxp/F8yllmbXGy1f9UugfiLhj09nijKssZgFJx2LLyjY2LG8l4Vj0tiVKyhcqK7loDOyxoVxF97LrK4ZzRbrTMSwRWmLWrOouVBMrXA3zWQVtqbBUEkxMKvI88KX79/HjJ18uKa6edIJ3XLyObeetpDXh0N2amOTjnY4w3Ghlh1MKNzo8kKUrFStZbD1pjxOZAqCtUtsSRKArCMe688aLiVkybQ+0iYS+3akaV7YlbJSCVCJGdzpRknEwW6QnnWAwW2RNV4przj+Dux47Mm3YVHj7OVrQXT+mWjwUtCW8vjvNf3vb7G9TaxVRYELD5s5SuYswCnge+P7jOiCjKxXj7a9fx1tfdQapuENXa2yc5TcTyimH06OFUnddgJUdLfRnCkHPMiFuWyxPJ2hLOqUv7lm9aZ47PozP1IXKQ2wLfF/7s/OuV7H85HDeK8XNRmX8qz94xSQF9qo1nVMquejt58r2pC4lOUUyScKxuGBd16Q2P7OhFhEFJjRs7iyVBBOjgGdIwfU5nS1MeUx70mH769Zxzau14u1MxehoqbzAVi0TlcP2HbsmfUnjjgUCm3vbSmMnMzlG8h6Xfe5+0nGbdNIhX/SnjQtOBMkMeoEwHsQe+6WwsRAFJQuvGgU23THlFrFilq5MVo62pNN01qUJDZsbS+UuwviAqyRX9Dg+lOPwwCiZaVJ7v/2+1/PvX7uWnrYka5el6EzF56x8yxH1WQ5lCzzfN0whiM09MZxDKcXJTI6+4QKpuE1nS4yir4jZFhuWt7I8HZ/y/Vd3tRB3bG7etoX3X76RjctbAUoLaRAmSwhn96ZrpnDK1TfoTJW/c+hOxaZsC29YmNSilvJCwFjA0xANJQN9G/TDx49U9KW+aUsP3ekEy1rjJJyZ+XlnSnir+9l79nCwP0vMsuhujTGYdTk2lKc/U8CxhZ50nJ62JEDJquxMxbnnw5ez+S9/Nu5zRF0MvW3JksURugR603H6MoVSD7q4LbS3xLh525aafa5yt5/DeY+4rTtXTOxGsdguSoNmKdxFGAVcgWgoGUC+6PHjJ49y58MvVSyGHrdhKOexqqN8RMJ8sHVLL7c9sJ/1QYLEy4NZLIS4rWOGC55PwrEYyhY5mcmXlNfgqHajnNWb5vm+TGnBTikd47u5N13yqW7fsavkEhjKTv7stbbtK91+rulM0t4yZrUrpRbdooxhaWEUcASlFMN5l8HRYqnATcH1uefpo/zDQy/RH0QYxGxdjWyiC7XgwZ6jg/UWm0MDo9gChwaygXtA4Vg6XC1mWRwdzOm0X3RpxYLn4/qKnXv7uHnbFj561xMM51xcz8cJOkRELdpwRVor8bEoizDvzQnCg2plrZRbxIoHlm+UxbgoY1haGB9wgOcrDp3KcnI4T9HzcT2fnz55lHfd/jBfvG+fvp23hGtefQbf+uPXV4xJHa20UjSPpOM2R07nxiUpFDyFLcKK9kSpHoJYWmkKwrLWsUphn7/2fC5Y18WqjhYuWNc1yacaxrWezOTHRUBYohM1BkeL82aJrnskKQAAC+JJREFUhue76ryVpgeaYdFhLOAA11e4vl7Aum9vH9948CAvn84BWtFsO28l77j4TFa2a19qJQXszTDGthZIJD15YmiZY1tBcXHts47bFj1tCdKJsdC06XxtoUsg7/rjzuFYFiKQ9/yaWqLlsqDueuwI1164mgf3nzKhXYZFg1HAEX61t4+vP/hiKV3XEnjTuSt45yVnsrpT+3Vb4jbLWqeOHqg3w3mX1Z1Jjg3mSjVzddiWT9FTbFreStFX4xa1Rgtu1UozdAnc9J3HGc65pSQPS/QPjmNZNbVEp2pEWYtYX4OhWTAKOODF/hE+89M9gPZvbj2nh+svWc+6bq2kYrZFdzo+Tok1C2HUwNkr20uLbXnXpzVul9KP5xpTuXVLb6meRMH1GM655F0f2xI+tHVTTS3RpZIFZTA0nzZpEGGq62VnLefdl57Jxh5dRcu2hM5UnPakMy+xvLUgGjXQlnRwbN3DLBo3WYvMrHpleC2VLCiDQVQ1+ahLgGVnnqu+/dP7OXuFziATEdqTDl2peKnpZZRzPnFP2foECcfi2f9y1bzLO5GwdsJi8I9GfcBRi30xBuIbFi1VWWvGAg5Y3dVSUr7phENXa5zYFFXKPrR1E1/45fPjogIkGG8E0y2kLaTaqqaWgmGpYBRwhERMt7pJxqbPYHvVmk4SjpBzx1RwItKFt5lYiLVVl0IWlMFg4oADYrawurOlKuULOv1Xt4DXCLogzWfv2TNvMs6WmdRW3bm3j+07dnHZ5+5n+45dpo2OwTCPGAUcYM1wge2FkyOlegjhSz2lx5uNcsVtykUVmF5mBkN9MQp4lrgl7cu4v6XxJqLaDg1LpQuBwdAsGAU8S+xoPcbwge591mxU22qnWkvZYDDUBqOAZ8lZPa3YER0Muv/app7WhslUiWprq5peZgZDfTFRELPklqvO5SN3PUEm7+L5CtsS0gmHW646t9GilaWaqIKl0oXAYGgWTCJGwEUXXaR27949o9cspuSHkMX4mQyGBlCVL9Io4IDZKGCDwWCoQFUK2PiADQaDoUEYBWwwGAwNwihgg8FgaBBGARsMBkODMArYYDAYGsSiVcAisk1EnhWRfSJyS6PlMRgMhoksSgUsIjbwZeAq4BXAdhF5RWOlMhgMhvEsSgUMvA7Yp5Tar5QqAN8BrmmwTAaDwTCOxaqAVwOHIs8PB2PjEJEbRWS3iOw+ceJE3YQzGAwGWOK1IJRSO4AdACJyQkRenOVbLQdO1kywudEssjSLHGBkKUezyAGLU5afK6W2TXfQYlXAR4C1kedrgrGKKKV6ZnsyEdmtlLpotq+vJc0iS7PIAUaWZpYDlrYsi9UF8QiwWUQ2iEgcuA64u8EyGQwGwzgWpQWslHJF5D8C9wI2cLtS6pkGi2UwGAzjWJQKGEAp9TPgZ3U63Y46nacamkWWZpEDjCzlaBY5YAnLYspRGgwGQ4NYrD5gg8FgaHqMAjYYDIYGYRTwHKlnzQkRWSsivxKR34nIMyLyZ8H4MhH5hYg8H/ztCsZFRG4NZHtSRC6ssTy2iDwuIj8Jnm8QkYeC8/1jEIGCiCSC5/uC/etrLEeniNwlIntFZI+IXNLAOfnz4P/maRG5U0SS9ZoXEbldRPpE5OnI2IznQUSuD45/XkSur6Esnw/+j54UkR+ISGdk38cCWZ4VkTdHxud0fZWTI7LvP4mIEpHlwfN5nZOyKKXMY5YPdITFC8BGIA48AbxiHs+3Crgw2G4DnkPXuvhb4JZg/Bbgc8H2W4B70O1RLgYeqrE8fwF8G/hJ8Py7wHXB9leBDwTbHwS+GmxfB/xjjeX4OvDHwXYc6GzEnKCzLQ8ALZH5eHe95gW4HLgQeDoyNqN5AJYB+4O/XcF2V41kuRJwgu3PRWR5RXDtJIANwTVl1+L6KidHML4WHSX1IrC8HnNSVr5aXghL7QFcAtwbef4x4GN1PP+PgH8DPAusCsZWAc8G27cB2yPHl46rwbnXAPcBVwA/Cb60JyMXWGlugi/6JcG2ExwnNZKjI1B6MmG8EXMSpsAvCz7nT4A313NegPUTlN6M5gHYDtwWGR933FxkmbDvbcC3gu1x1004L7W6vsrJAdwFnA8cZEwBz/ucTHwYF8TcqKrmxHwQ3K5eADwErFBKHQ12HQNW1EG+/wH8Z8APnncDp5VSbplzleQI9g8Gx9eCDcAJ4GuBO+TvRaSVBsyJUuoI8HfAS8BR9Od8lMbMS8hM56Fe3+n3oK3NussiItcAR5RST0zYVfc5MQp4ASIiaeCfgA8rpYai+5T+iZ7X2EIReSvQp5R6dD7PUyUO+hbzK0qpC4AR9K12iXrMCUDgX70G/aNwBtAKTFsPoF7Uax6mQ0Q+DrjAtxpw7hTwl8An633uchgFPDdmXHNirohIDK18v6WU+n4wfFxEVgX7VwF98yzfG4CrReQgutTnFcAXgU4RCZN7oucqyRHs7wD6ayAHaGvksFLqoeD5XWiFXO85AXgTcEApdUIpVQS+j56rRsxLyEznYV6/0yLybuCtwNuDH4R6y7IJ/QP5RPD9XQM8JiIr6ywHYBTwXKlrzQkREeD/AHuUUv89sutuIFyZvR7tGw7H3xWs7l4MDEZuR2eNUupjSqk1Sqn16M98v1Lq7cCvgGsryBHKd21wfE0sMaXUMeCQiJwTDP0+8DvqPCcBLwEXi0gq+L8KZan7vESY6TzcC1wpIl2BRX9lMDZnRGQb2m11tVJqdIKM1wVRIRuAzcDDzMP1pZR6SinVq5RaH3x/D6MXto/RgDmZsxN5qT/QK6fPoVdrPz7P57oMfQv5JPAvweMtaL/hfcDzwC+BZcHxgu4M8gLwFHDRPMi0lbEoiI3oC2cf8D0gEYwng+f7gv0bayzDq4Hdwbz8EL1S3ZA5AT4F7AWeBr6JXtmvy7wAd6J9z0W0YnnvbOYB7Z/dFzxuqKEs+9C+1PC7+9XI8R8PZHkWuKpW11c5OSbsP8jYIty8zkm5h0lFNhgMhgZhXBAGg8HQIIwCNhgMhgZhFLDBYDA0CKOADQaDoUEYBWwwGAwNwihgw4JGRFaIyLdFZL+IPCoiD4rI2xos0w9FZFcjZTAsDIwCNixYgmSHHwIPKKU2KqVegw7WX1Pl62vekisosfgaoENENtbrvIaFiVHAhoXMFUBBKfXVcEAp9aJS6n+KyHoR+bWIPBY8LgUQka3B+N3oLLXQYn1UdB3fG8P3EpH3ishzIvKwiPxvEflSMN4jIv8kIo8EjzdEZPq3wI/RKdrXRd7rDhH5qog8BPytiLQGtWofDooIXRMcV1ZuwyKllllA5mEe9XwANwFfqLAvBSSD7c3A7mB7K7pgz4bIsWF2WAs6g60bXUznILq0ZAz4NfCl4LhvA5cF2+vQqeHhe/0C+FfA2cBTkfE70OUp7eD5fwXeEWx3orO9WivJbR6L82FuhQyLBhH5Mjpdu4AujPMlEXk14KEVYsjDSqkDkec3RfzGa9GKbyXwz0qpU8F7fy/yHm8CXqE9IAC0BxXqWoPX/j+llBKRooicp5QKuzF8TynlBdtXogsafSR4nkQr85enkNuwyDAK2LCQeQb4o/CJUupDotvL7Ab+HDiOLrptAbnI60bCDRHZilaolyilRkVkJ1oZToUFXKyUir4nInIDug7FgUA5t6OLeX984nnRdQf+SCn17IT3+Jsp5DYsMowP2LCQuR9IisgHImOp4G8HcFQp5QPvRLe3KUcHMBAo3y3oVjSgK3G9MaiA5RBR9MD/Bf40fBJYq6CV7TY1VmkrXBQsx73AnwYLiYjIBTOU27AIMArYsGBRSingD9GK8oCIPIzuD3cz8L+A60XkCWAL463PKD8HHBHZA3wW2BW89xG0n/Zh4Ddof/Bg8JqbgItEN278HfAnojuUnBm+PniPA8CgiLy+zHk/g/YtPykizwTPmYHchkWAqYZmMFRARNJKqUxgAf8AuF0p9YNGy2VYPBgL2GCozN+IyL+gIyMOoGOODYaaYSxgg8FgaBDGAjYYDIYGYRSwwWAwNAijgA0Gg6FBGAVsMBgMDcIoYIPBYGgQ/x8a92IcIzVvMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a415780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相关性，跟房价关系挺大的\n",
    "sns.lmplot('GarageArea', 'SalePrice', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a73bcc0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3009e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分布，拟正太分布，本身特征的分布\n",
    "# 核概率分布曲线 \n",
    "sns.distplot(train['GarageArea'], color='y', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a769be0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6fa470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 能放多少量车，3个车位\n",
    "sns.violinplot(train['GarageCars'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "garage_cols=['GarageType','GarageQual','GarageCond','GarageYrBlt','GarageFinish','GarageCars','GarageArea']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GarageType GarageQual GarageCond  GarageYrBlt GarageFinish  GarageCars  \\\n",
       "39        NaN        NaN        NaN          NaN          NaN           0   \n",
       "48        NaN        NaN        NaN          NaN          NaN           0   \n",
       "78        NaN        NaN        NaN          NaN          NaN           0   \n",
       "88        NaN        NaN        NaN          NaN          NaN           0   \n",
       "89        NaN        NaN        NaN          NaN          NaN           0   \n",
       "\n",
       "    GarageArea  \n",
       "39           0  \n",
       "48           0  \n",
       "78           0  \n",
       "88           0  \n",
       "89           0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[garage_cols][train['GarageType'].isnull()].head()\n",
    "# print(train[basement_cols+['TotalBsmtSF']][train['BsmtQual'].isnull()==True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in garage_cols:\n",
    "    if train[col].dtype==np.object:\n",
    "        train[col]=train[col].fillna('None')\n",
    "    else:\n",
    "        train[col]=train[col].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PoolQC         1453\n",
      "Fence          1179\n",
      "MiscFeature    1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a751e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.PoolQC 泳池质量 填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PoolArea PoolQC\n",
       "0         0    NaN\n",
       "1         0    NaN\n",
       "2         0    NaN\n",
       "3         0    NaN\n",
       "4         0    NaN"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.filter(like='Pool', axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d4ce9b0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d56cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['PoolArea'], color='g', kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根本没有泳池\n",
    "train.PoolQC=train['PoolQC'].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fence          1179\n",
      "MiscFeature    1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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aPUiSRqZ+DKCqtgPbp/2+Apxa05HNz+eUpaqG7kGSNAC/CkKSOmUASFKnDABJ6pQB0IEkz0vy8rZ8bJK/O3RP0rz2mXz+0H30yABY5pL8K+B64HdaaQ3wB8N1JH1bkn8K3AF8vK3/eBKvDZoSA2D5uxh4KfBlgKq6H3jOoB1J3/YORl8S+RhAVd0BnDZkQz0xAJa/r7dvXgUgyUrAc391pPhmVT2+T83P55QYAMvfnyZ5O3BsklcAvwf84cA9SfPuSfLPgBVJ1id5L/B/hm6qF14Itswl+T7gIuAcIIy+huP95T+8jgBJjgN+jdHnE0afz/9YVV8brqt+GADLXJJnAV+rqqfa+grg6Kp6YtjO1Lv2WXxPVf3boXvplVNAy99O4Nix9WOBPxmoF+lb2h8l/3joPnp2xN0QRkvumKr6yvxKVX2l7XZLR4Lb22mfvwd8db5YVR8ZrqV+GADL31eTbKiq2wCS/EPgbwfuSZp3DPAl4KyxWgEGwBR4DGCZS/Ji4Drg/zE6CPwDwM9V1a2DNiZpcAZAB5J8PzB/qf1nq+qbQ/YjzUvyu+znvP+q+oUB2umOU0B9eDGwjtG/94YkVNU1w7YkAfCxseVjgJ9itLeqKXAPYJlL8j+BH2L0fStPtXJV1ZuG60rav3bdymeq6h8N3UsP3ANY/jYCp3vhl75HrMfvqpoaA2D5u5vRgd8Hh25E2leSv+E7jwE8BLx1oHa6YwAsfycD9yb5c+Dr88Wqes1wLUkjVeW9KQZkACx/7xi6AelAkuysqrMXqunwMACWuar60yTPA9ZX1Z+0q4BXDN2X+pbkGOA44OQkqxhdowJwPLB6sMY6YwAsc+2OYFuAkxidDbQa+G3Av7A0pF8EfgX4QeBWvh0AXwb+x1BN9cbTQJe5JHcwuuPSzVX1ola7q6r+wbCdSZDkl6rqvUP30Sv3AJa/r1fVN5LRH1jeEUxHkqp6b5IXAKczuhBsvu6FilNgACx/+94R7A14RzAdIZJcCryMUQBsB84DPgMYAFPgFNAy5x3BdCRLchfwQuD2qnphklOA/1VVrxi4tS64B7BMJXluVX2hqp4G3tce0pHmb6vq6SRPJjkeeBhYO3RTvfCOYMvXH8wvJPn9IRuRDmJXkhMZ/YFyK3Ab8GfDttQPp4CWqSS3j531861l6UiVZB1wfFXdOXAr3XAPYPmqAyxLR4yM/PMk/76qPgc8luSMofvqhXsAy1SSpxjdYzWMbgT/xPwmRl8HffxQvUnzklwBPA2cVVU/2q4K/uOqevHArXXBg8DLVFX5dQ/6XvCSqtqQ5HaAqno0yVFDN9ULp4AkDembSVbQpimTzDDaI9AUGACShnQ58FHgOUnexegisP80bEv98BiApEEl+RFGX04YYGdV3TdwS90wACRNXZKfrqqPtOVVVfXo0D31yCkgSUP4d2PLOwfronMGgKQh5ADLmiJPA5U0hGOTvIjRH6HHtOVvBUFV3TZYZx3xGICkqUvyyYNsrqo6a2rNdMwAkKROeQxA0mCSXNy+DXR+fVWSNwzZU0/cA5A0mCR3VNWP71Pz22unxD0ASUNakfkbVgPtayH8LqAp8SwgSUP6OPChJL/T1n+x1TQFTgFJGky7Z/UW4OWttIPRPaufGq6rfhgAko4ISU4C1nhHsOnxGICkwST5VJLj23/+twLvS3LZ0H31wgCQNKQTqurLwE8D11TVSxh9M6imwACQNKSVSU4FXgt8bOhmemMASBrSO4EbgdmquiXJ3wPuH7inbngQWJI65XUAkqYuyVuq6j8neS/tfsDjqupNA7TVHQNA0hDmb/u4a9AuOucUkCR1yj0ASVOXZNvBtlfVa6bVS88MAElD+AlgN3AtcDPeFnIQTgFJmrr2rZ+vAC4Efgz4I+Daqrpn0MY643UAkqauqp6qqo9X1WbgTGAW+FSSNw7cWlecApI0iCRHA69mtBewDrgc+OiQPfXGKSBJU5fkGuAFwHbguqq6e+CWumQASJq6JE8DX22r4/8JBaiqOn76XfXHAJCkTnkQWJI6ZQBIUqcMAEnqlAEgSZ0yACSpU/8fFWRYTOPe6zMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d6b6470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.Fence 围栏填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d3bd860>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d4c5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(train['Fence'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总共有5个类型，然而可以统计出来的类型一共只有4类，可以断定最后一类NA用空值代替了\n",
    "\n",
    "有没有围栏，价格都差不多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.Fence = train['Fence'].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a645978>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d575128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 回看填充后的数据分布 与 SalePrice的关系\n",
    "sns.violinplot(train['Fence'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MiscFeature    1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119eb6860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getNullCount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10.MiscFeature 其余特征，备注特征填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a602208>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0c3320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看看它的类型与 SalePrice 全不全\n",
    "sns.violinplot(train['MiscFeature'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为MiscFeature本来有5类，然而只统计出四类，因此可以断定第五类NA其实是空值代表的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.MiscFeature = train['MiscFeature'].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getNullCount() 没空值了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存空值处理后的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#不保存行索引\n",
    "train.to_csv('datas/house_data.csv', index=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4rc1"
  },
  "toc-autonumbering": false
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
